# Restaurant food waste valorization by microwave-assisted hydrolysis: Optimization, typological and biochemical analysis

Payam Torabi, Nasser Hamdami, Nafiseh Soltanizadeh, Omidvar Farhadian, Alain Le-Bail

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Payam Torabi, Nasser Hamdami, Nafiseh Soltanizadeh, Omidvar Farhadian, Alain Le-Bail. Restaurant food waste valorization by microwave-assisted hydrolysis: Optimization, typological and biochemical analysis. Cleaner Materials, 2024, 13, pp.100269. 10.1016/j.clema.2024.100269. hal-04754641

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# Restaurant food waste valorization by microwave-assisted hydrolysis: Optimization, typological and biochemical analysis

Payam Torabi<sup>a</sup>, Nasser Hamdami<sup>a,b,*</sup>, Nafiseh Soltanizadeh<sup>a,*</sup>, Omidvar Farhadian<sup>c</sup>, Alain Le-Bail<sup>b</sup>

## ARTICLE INFO

### Keywords:

- Food waste

- Valorization

- Microwave

- Hydrolysis

- Optimization

## ABSTRACT

Annually, a substantial volume of food waste is being released into the environment. Restaurant food waste (RFW) valorization using microwave-assisted hydrolysis (MAH) is a sustainable approach to produce fermentable sugars. However, RFW is composed of different foodstuffs with different physicochemical, nutritional, and degradation rates. This study explored the typological, chemical, and elemental analysis of RFW. Results revealed that the four main types of RFW were vegetable (33.2 %), meat (19.3 %), rice (15.2 %), and bread waste (11.0 %). The key parameters impacting the MAH of typologically sorted RFW were identified using the Plackett-Burman design (PBD). Then the central composite design (CCD) with 30 runs was used to increase reducing sugar content (RSC). The optimized condition was as follows: temperature 96.0 °C, microwave power 340 W, HCl concentration of 1.45 %, and microwave heating time 11.1 min. The derived hydrolysates were characterized for their biochemical and monosaccharide composition.

## Introduction

Food waste (FW) is generated through home cooking, at stores, restaurants, fast food chains, cafeterias, catering, and other places where food is stored, prepared, or processed. Approximately one-third (2.5 billion tons) of food is lost or wasted annually (Kumar et al., 2022). Iran as a developing country is estimated to dispose of FW at a rate of about 25 million tons per year (or 30 % of production) (Fami et al., 2019), which is worth about 5–8 billion dollars (Ghaffari et al., 2020). Due to a lack of financial, technical, and managerial resources, it is generally accepted that developing countries like Iran experience more food loss during the earlier stages of the food supply chain, such as production and postharvest handling. In contrast, FW at the consumer stage is generally thought to be low, especially when compared to that of developed countries. However, according to previous studies, FW at the consumption stage in Iran is substantial (Alavi Moghadam et al., 2009; Farzadkia et al., 2012). Due to the restaurant's increasing visibility in society and its growing role in Iran's FW production, our study focuses solely on restaurant waste.

The RFW is composed of different foodstuff groups with different

physicochemical, biostability, nutritional, and elemental properties. For example, vegetable/fruit waste has high fiber; bread, and rice composed the high amount of starch; and meat/fish waste has high protein content. This variation in the composition of FW influences its biochemical properties, significantly impacting the efficiency and development of digestion processes (Bayard et al., 2018; Fisgativa et al., 2017). Typological analysis is recommended by previous literature (Bayard et al., 2018; Carmona-Cabello et al., 2020; Fisgativa et al., 2017) to evaluate the distribution of different foodstuff groups in the collected biowastes. Although previous studies represent the typological composition of FW from European restaurants or fast foods, no study has been conducted on Iranian restaurants. In the current study, FW from Iranian restaurants was first typologically classified, and the foodstuff groups with the highest frequency were chosen as feedstock for biorefineries.

Environmental issues like toxicity to animal life (Salemdeeb et al., 2017), pollution of surface and ground waters (Nayak and Bhushan, 2019; Sathish et al., 2024), air pollution (Grizzetti et al., 2013), global warming (Amicarelli et al., 2021), and reduced soil quality (Eden et al., 2017) come from improper disposal of FW by landfill and incineration. Although preventing FW is the best course of action, Iranian

<footnote><sup>a</sup> Department of Food Science and Technology, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran</footnote>

<footnote><sup>b</sup> ONIRIS - GEPEA (UMR CNRS 6144), Site de la Géraudière CS 82225, 44322 Nantes Cedex 3, France</footnote>

<footnote><sup>c</sup> Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: nasser.hamdami@oniris-nantes.fr (N. Hamdami), soltanizadeh@iut.ac.ir (N. Soltanizadeh).</footnote>
technological, economic, and cultural issues make it impossible to prevent FW. In this regard, recycling FW to recover nutrients for biotechnological processes because it is high in carbs and proteins would be the ideal choice (Chen et al., 2022). However, starch and protein from FW cannot be consumed by microorganisms like bacteria, mold, yeast, or microalgae (Chen et al., 2022). Therefore, macromolecules are required to be transformed into less complex substances like fermentable sugars.

Several procedures, including acidic hydrolysis (He et al., 2012), hydrothermal liquefaction/carbonization (Wang et al., 2022), enzymatic hydrolysis (Salimi et al., 2021; Zhang et al., 2020), and aerobic digestion (Ren et al., 2018; Srisowmeya et al., 2020) have been developed as hydrolysis step. All of these methods have their drawbacks like the excessive cost of enzymes, the formation of furans derivatives by acids, and the high operating cost of aerobic digestion (Liu et al., 2021). To facilitate the hydrolysis of FW, numerous studies have concentrated on physical, chemical, and biological pretreatments. Microwave pretreatment, which has good performance in terms of low energy consumption, green process, and ecologically friendly has lately been employed for lignocellulosic biomass pretreatment (Prasertsilp et al., 2023). However, to the best of our knowledge, there has been no current report on the pretreatment of RFW using microwave treatment to date.

The purposes of the current study were to (1) bring a complete overview of RFW by physicochemical, typological, and elemental composition analysis; (2) optimize the microwave-assisted hydrolysis (MAH) of typologically sorted RFW for maximizing the yields of reducing sugar content (RSC) by using Plackette-Burman design (PBD) and Response Surface Methodology (RSM); (3) identify and quantify the monosaccharide composition of the derived hydrolysate by HPLC analysis; (4) Conduct a complete biochemical analysis of the derived hydrolysates. It is the first study that includes RFW typological and biochemical composition analysis, provides an overview of how the MAH affects different foodstuff groups, and investigates the characteristics of the resulting hydrolysates.

## Material and methods

### Food waste (FW)

### Collection and sampling

The FW used in this investigation was collected from Iranian restaurants in Isfahan City (Iran). Isfahan is the 3rd most densely populated city in Iran and the largest city in the country's center. Restaurants involved in the study were Iranian restaurants, mainly serving Persian foods. Three well-known, highly frequented restaurants provided organic waste. Lunch and dinner were included in the analysis, whereas breakfast was excluded due to its limited availability in only a few restaurants. Items such as tissues, glassware, plastics, and other inorganic materials were segregated from the waste. The samples collected from the restaurants were combined for further analyses. 50 kg of kitchen waste (KW) and unfinished plate (UP) were collected separately and kept at 4 °C for up to one day before the typological sorting.

### Typological sorting

RFW was manually sorted in the Food Engineering Laboratory (Isfahan University of Technology) using the method of Fisgativa et al. (2017). RFW was initially separated into nine fractions: Vegetables (VW), fruits (FRW), rice (RW), meat (MW), fish (FIW), bread (BW), cooked food (CFW), drinks (DW), and others. Then, measurements were made of how each of these fractions was distributed among UP and KW. The proportion of each fraction was calculated by Eq (1).

$$ \text{Fraction yield} = \frac{\text{Wet weight of fraction}}{\text{Total weight of RFW}} \times 100 \quad (1) $$

After typological classification was done, 4 main fractions that had the most frequency were chosen for MAH and characterization. For this

purpose, no distinction was made between KW and UP.

For the characterization of the RFW and the MAH experiments, wet samples were ground using a mill (sieved by 40-mesh size) and subsequently dried at 60 °C (IKA, Germany) until constant weight was achieved. Dried samples were stored in plastic bags at -20 °C to prevent microbial or chemical reaction-induced deterioration before experiments.

### Proximate and elementary analysis

The AOAC method was used for proximate analysis (AOAC, 2000). The starch content was determined according to the acid hydrolysis procedure. Elementary analyses of carbon (C), nitrogen (N), hydrogen (H), oxygen (O), sulfur (S), magnesium (Mg), phosphorus (P), potassium (K), and calcium (Ca) were performed using an EDAX analyzer (Philips X130, Netherland).

### Microwave-assisted hydrolysis (MAH)

After transferring 50 g of the dried RFW powder to a 500 mL, 3-neck, flat-bottom glass flask, the solvent was added to the flask and heated by microwave at 2450 MHz. Samples were hydrolyzed at various conditions. A spinning reflux condenser was connected to the top of the microwave oven to prevent water evaporation. Once the hydrolysis process was finished, the temperature of hydrolysate was reduced to room temperature. After that, the liquid fraction was separated using centrifugation (TZ36 HK, Hermle Co., Ltd., Germany) for 15 min at 9000 × g, and a vacuum pump was used to filter it through cellulose acetate filter paper. The resulting hydrolysates were kept at -20 °C.

## Optimization study

### First step: Plackette-Burman design (PBD)

A two-step optimization strategy was used to optimize the MAH of various RFW. The PBD is a helpful method for choosing significant variables in optimization studies, where a small number of runs and many parameters are involved. In this study, the PBD was used as an initial optimization step to determine the variables that significantly impact the hydrolysis of polysaccharides. The studied variables were solvent/waste ratio (mL/g), mixing speed (rpm), solvent type, temperature (°C), solvent volume (mL), microwave power (W), microwave heating time (min), and solvent concentration (% v/v). These variables were selected based on previous studies that investigated the use of microwave treatment to enhance the hydrolysis rate of waste. The experimental design of the PBD was generated and analyzed using Minitab software (Minitab Inc., USA) to select significant factors. Variables with confidence levels above 95% were considered significant and were included in the next optimization step. Every variable was studied at -1 for the low level and +1 for the high level. The effects of the mentioned parameters on the RSC of each hydrolysate were studied using the first-order model:

$$ y = \beta_0 + \sum \beta_i x_i \quad (2) $$

### Second step: Central composite design (CCD)

The secondary optimization stage aimed to determine the effects of MAH significant variables on the RSC of various hydrolysates. For this purpose, a 30 run CCD was established using four significant variables defined as X₁, X₂, X₃, and X₄, respectively. The low, middle, and high levels of each variable were considered as -2, -1, 0, +1, and +2, respectively. The coefficients of the regression equation were evaluated using Minitab software (Minitab Inc., USA). All 30 runs' data were examined using multilinear regression in a complete quadratic model for each response:

$$ y = \beta_0 + \sum_{i=1}^{n} \beta_i x_i + \sum_{i=1}^{n} \beta_{ii} x_i^2 + \sum_{i \neq j}^{n} \beta_{ij} x_i x_j \quad (3) $$
where y represents the response, β₀, βᵢ, βᵢᵢ, and βᵢⱼ are the model constant, the coefficient for the variables xᵢ (linear terms), the coefficients for the variables xᵢⱼ (quadratic term), and the coefficients for the variables xᵢⱼ (first-order interaction terms), respectively.

The coefficient of determination (R²), adjusted coefficient of determination (R² adj), and predicted coefficient of determination (R² pred) have all been used to pick the best model from all the sets of created models.

## Analytical methods

A pH meter was used to determine the pH of the hydrolysates. Chemical oxygen demand (COD) was analyzed by a COD analyzer (DR2500, HACH, USA), and biological oxygen demand (BOD) was analyzed by a BOD analyzer (BOD TrackTM, HACH, USA). TDS and TSS were estimated by gravimetric method. The electrical conductivity (EC) was measured using an EC meter (WTW cond 7110, Germany). Quantifying nitrate and phosphate content in the medium was determined using the spectrophotometric standard method for examining water and wastewater (Azizi et al., 2021). The ammonia content was obtained using Nessler's reagent methods. Free amino nitrogen (FAN) concentration was measured using the ninhydrin reaction method (Hill and Stewart, 2019). The 3,5-dinitrosalicylic acid (DNSA) technique was used to calculate the RSC (Jain et al., 2020). The percentage of carbohydrate recovery (CR) (w/w) in hydrolysates was calculated using Eq. (4).

$$CR (\%, \frac{w}{w}) = \frac{\text{hydrolysate (g)}}{\text{RFW (g)}} \times 100 \quad (4)$$

## Monosaccharides composition of hydrolysates

The monosaccharide composition of different hydrolysates was determined using the high-performance liquid chromatography (HPLC) technique. First, hydrolysates were treated with a 2 M trifluoroacetic acid (TFA) solution at 85 °C for 3 h and then chilled in an ice-water bath and then centrifuged for 20 min at 3000 × g. Next, hydrolysates were freeze-dried to extract the TFA. Once the dried hydrolysates were dissolved in deionized water, an HPLC system included a Smartline RI detector 2300 (Knauer, Berlin, Germany), and a Eurokat H column was used for monosaccharide composition analysis. The mobile phase was sulfuric acid (0.005 M) at a flow rate of 0.8 ml/min at 70 °C. Finally, the standard monosaccharides, including glucuronic acid (GluA), galacturonic acid (GalA), arabinose (Ara), galactose (Gal), rhamnose (Rha), xylose (Xyl), glucose (Glu), and fructose (Fru) were used to estimate the monosaccharide composition based on their retention times.

## Functional group analysis

The FT-IR measurements of RFW and the resulting hydrolysates were performed using a Tensor 27 FT-IR spectrometer. The infrared spectra were obtained at room temperature, with air serving as the reference, over a wavenumber range of 400 to 4000 cm⁻¹.

## Statistical analysis

The study's findings are displayed as means ± standard deviation (SD). Using IBM Corporation's (New York, USA) SPSS software, version 20, an analysis of variance (ANOVA) was carried out. P-values less than 0.05 were regarded as statistically significant. The differences between the treatment means were ascertained using Duncan's New Multiple Range Test.

# Results and discussion

## Characterisation of the RFW samples

### Typological composition

Although there are many ways to handle problems with RFW management, prevention and valorization are two of the most essential options (Carmona-Cabello et al., 2020). To value RFW and reuse it as a culture medium for biological processes, identifying different foodstuffs in the RFW is necessary because it affects the chemical composition and possible paths for valorization (Carmona-Cabello et al., 2020).

The results of the typological analysis are shown in Fig. 1. The VW had the highest percentage of FW (33.2 %). Wang et al. (2017) also reported a similar finding, noting that vegetables accounted for the largest share of RFW, comprising 29 %. Vegetables in KW mainly included non-cooked rests, peels, stems, roots, and inedible parts of vegetables, as well as rotten vegetables. While salad and the side dishes leftovers made up most of the VW on UP. Meat was also characterized by a high percentage of waste, 19.3 %. Meat wastage in the kitchen was almost 2.15 times more than MW from UP. In fact, due to the high price of meat in Iran, people consume meat more carefully and leave fewer leftovers, which include bones and pieces of meat and cartilage attached to bone. In Iranian cuisine, rice is an essential meal and is offered with most dishes. On the other hand, a significant amount of rice is lost during consumption. The reasons that follow may be the cause of this waste: (1) rice is rich in carbohydrates, and some dieters choose to eat very little of it; (2) large portions of rice result in a lot of leftovers; and (3) rice is less expensive than main dishes, so people are less likely to finish it (Bozdağ and Cakiroglu, 2020). Iran is one of the nations with the most varieties of bread and a high per capita consumption. The four types of Iranian bread that are most popular are Tufton, Lavash, Sangak, and Barbari bread. But for serving, restaurants primarily employ lavash and Tufton bread (Eslamizad et al., 2019). In comparison to the waste at the consumer level (9.63 %), Fig. 1 illustrates how a small amount of BW was generated in the kitchen (1.37 %). Due to the time and expense of producing bread, restaurants prefer to purchase it. This way, there was no waste in the kitchen during the preparation and baking of bread, and only a tiny portion of the bread may retrograde if left unused and unable to be served. Bread wastage by consumers comes down to cultural customs and personal taste (Ghaziani et al., 2022). For example, price-conscious people can think that some meals like bread or rice are inexpensive, which could discourage their consumption. CFW is referred to as stew dishes in this study. Based on the typological analysis, a high degree of inefficiency was found in the restaurants under study, as evidenced by the quantity of food that was made but never served, which is known as incorrectly processed and discarded meals. By contrast, CFW at the consumer level was almost 11.26 times lower than in the kitchen.

The typological composition of RFW from this study was compared with that of previous studies. Gaiani et al. (2018) reported that the household FW in Italy consisted of 24 % vegetables, 17 % cheese, 16 % bread, 15 % milk, 14 % yogurt, and 14 % meat. According to Fisgativa et al. (2017), 24 % of paper, 9.7 % of vegetables, 29.6 % of fruits, 20.9 % of starchy foods, 7.5 % of meat, and 8.4 % of others were identified in collective catering of Rennes, France. Wang et al. (2017) surveyed RFW in four Chinese cities (Beijing, Shanghai, Chengdu, and Lhasa). According to their results, RFW composition was: 29 % vegetables, 14 % rice, 11 % aquatic products, 10 % wheat, 8 % pork, 7 % beans, 6 % poultry, 5 % potatoes & tubers, 3 % beef, 2 % mushrooms & fungi, 1 % fruits, and 1 % maize. Vavouraki et al. (2013) also reported that KW of students' restaurants in Greece consisted of 46.8 % vegetables, 3.2 % meat, 15.6 % cooked food (4.2 % meat, 0.9 % fish, 2.1 % legumes, 3.7 % potatoes, 1.9 % rice, and 2.8 % others), 14.5 % fruits, 9.7 % salads, 8 % bread, and 2.2 % dessert.

Further research is necessary to determine the impact of general consumer characteristics, such as gender, age, education, and
<figure><img src="image_3.png" /><figcaption>Fig. 1. Typological analysis of RFW composition.</figcaption></figure>

employment position, on the generation of FW in Iranian restaurants (Fami et al., 2019). RFW's typological composition is variable because of the changing composition of restaurant waste caused by the variables above. For example, traditional restaurants could contain more BW. On the other hand, a restaurant serving meat as primary food might have a lot of MW, a vegan restaurant might have a lot of vegetable waste, or a seafood restaurant might have a lot of fish waste. So, we chose to investigate the four primary types of FW's chemical and elemental analyses as well as the ensuing hydrolysis process independently for the following reasons: (1) The chemical composition of FW can affect the effectiveness of the hydrolysis process, and (2) The availability of raw materials is a crucial factor for the biorefining sector. Not every city, area, or country will have every type of FW, based on numerous factors such as economic situations, cultural norms, and customs. According to the results of typological analysis, VW, MW, RW, and BW were characterized as the most abundant types of RFW and were selected as feedstock for further studies and hydrolysis steps.

## Chemical composition

Since FW chemical composition is directly related to the waste's capability for degradation through chemical or biological processes, it serves as the foundation for all valorization procedures in the bio-refinery concept (Carmona-Cabello et al., 2020). The chemical composition of each fraction considered in this study is displayed in Fig. 2A. The primary component of the biomass from BW, RW, and MW was carbohydrates, which were divided into four groups: sugars, reducing sugars, starch, and fiber. Carbohydrates were also identified by Fisgativa et al. (2017) as the primary macronutrient of FW collected from restaurants, accounting for 69.7 %. According to previous studies, carbohydrates from FW could be hydrolyzed into glucose as a source of natural carbon and energy for microorganisms in many biological processes like anaerobic digestion, bioethanol production, etc (Fisgativa et al., 2017; Taheri et al., 2021; Zhang et al., 2020). In BW and RW, starch was the main constituent of the carbohydrate family. The BW samples had the highest starch concentration (61.09 % w/w, dwt), followed by the RW samples (50.89 % w/w, dwt), and finally, the VW samples (7.60 % w/w, dwt). A high content of starch in RFW was stated by Salimi et al. (2021), representing an average of 63.92 % (w/w, dwt). As anticipated, since starch is a carbohydrate generated by different green plants for energy storage (Carmona-Cabello et al., 2020), none was detected in the MW samples. Vegetables, fruits, and cereals include

carbohydrates called fiber that serve structural purposes in plant cell walls. VW samples had the highest fiber content (38.86 % w/w, dwt), whereas MW samples had the lowest value (1.20 % w/w, dwt). Compared to other wastes under study, the fiber level of VW samples was about fourteen times higher. The prevalence of different types of vegetables in VW samples may have contributed to these variations in the proportion of fiber among the four categories of waste. However, high fiber content may limit waste's ability to decompose, according to Prasertsilp et al. (2023). A class of carbohydrates known as "reducing sugars" includes all monosaccharides and sure di-, oligo-, and polysaccharides that can function as reducing agents. RW samples proposed the highest RSC with a value of 7.52 % (w/w, dwt), followed by VW with 5.92 % (w/w, dwt) and BW and MW, with values of 4.37 % (w/w, dwt) and 0.72 % (w/w, dwt), respectively. Carmona-Cabello et al. (2020) reported that the RSC of FW from restaurants varied from 2.4 % (w/w, dwt) to 3.8 % (w/w, dwt). Additionally, Salimi et al. (2021) reported an RSC of 7.82 % (w/w, dwt) for FW from typical Greek restaurants.

Proteins in FW are an excellent source of nitrogen essential to microbial fermentation. According to earlier research, meat and meat products are the primary sources of RFW's protein content (Carmona-Cabello et al., 2020; Fisgativa et al., 2017). Nevertheless, MW samples had the highest protein value at 40.71 % (w/w, dwt), followed by VW, RW, and BW at 17.49, 4.33, and 9.17 % (w/w, dwt), respectively. Despite having a significantly lower protein content than MW samples, non-animal wastes were nevertheless a helpful source of protein. Proportions of plant-origin food fractions, such as skin, rind, pomace, beans, nuts, and seeds could explain the protein values in the VW samples. As to the findings of Miramontes-Martínez et al. (2021), VW is linked to low protein content and easily digestible carbohydrates whereas slaughter-house waste (SHW) had a high protein content (49.04 %) and low carbohydrate content (7.12 %) which complied with the present study. Sahoo et al. (2021) also reported the crude protein content of VW to be 81.3 g/kg DM. Additionally, BW is a perfect substrate for microbial fermentations since it contains pure, high-quality proteins. Proteins make up 8–10 % of bread, according to Narisetty et al. (2021). In the case of RW samples, the lowest protein content was found in bread. However, rice, wheat, oats, and milk, are reported as the primary sources of protein globally due to their essential amino acid profiles. According to Kamal et al. (2021), the protein content of cooked rice was 2.6 %, which was less than what was found in this investigation.

FW lipids present a profitable alternative as low-cost, renewable
<figure><img src="image_4.png" /><figcaption>Fig. 2. Proximate (A) and elemental (B) analysis of typologically sorted RFW.</figcaption></figure>

feedstocks for biodiesel production (Lin et al., 2021). According to Fis-
gativa et al. (2017), the lipid content of FW collected from French res-
taurants was 17.3 % (w/w, dwt). Additionally, FW from Italian
restaurants has a lipid content of 25.7 % (w/w, dwt) (Carmona-Cabello
et al., 2020). Furthermore, as stated by Salimi et al. (2021), the lipid
content of FW from Greek restaurants was 10.55 % (w/w, dwt). With an
average lipid concentration of 7.32 % (w/w, dwt), MW samples had the
highest concentration of lipids. The presence of meats, frying oils, or
sauces used in preparing meat-based meals is closely related to this high
variation (Carmona-Cabello et al., 2020). The lipid content of SHW was
found to be 41.04 % by Miramontes-Martínez et al. (2021), which was
greater than that of MW in the current investigation. Although edible oil
plants like canola, soybean, sunflower, etc., are known for their high

lipid content, the VW driver from restaurants mainly comprises grass
and leafy vegetables with low lipid content. In the case of BW samples,
even though lipid content is low, lipids in bread are derived from many
constituents. In a standard bread recipe, shortening, surfactants, and
wheat flour are the three primary sources of lipids (Pareyt et al., 2011).

Each foodstuff has a unique composition of minerals. Ash content
illustrates the metal and mineral fractions found in organic materials.
According to gravimetric analysis, MW had the highest value at 29.30 %
(w/w, dwt), followed by VW at 9.25 % (w/w, dwt), and the lowest
values were found in the RW and BW samples. Carmona-Cabello et al.
(2020) reported that the ash content of FW from Italian restaurants was
5.3 %. Furthermore, according to Miramontes-Martínez et al. (2021),
7.14 % of the ash was made up of food and vegetable waste. Animal
waste has a higher ash concentration than plant waste, as demonstrated by Islam et al. (2023). Their findings showed that the crude ash content of the bone waste from cows and chickens was 41.26 % and 39.10 %, respectively, while the concentration for the mixed vegetables was 5.19 %, which is in line with the conclusions of the current analysis.

## Elemental composition

The primary structural component of biomolecules like proteins, lipids, and carbohydrates is carbon (C). According to Fig. 2B, the C content of RFW varied from 26.18 to 38.24 %. The C contents of plant-based waste were greater than those of MW samples. The results were comparable to those of Islam et al. (2023), who found that the amounts of C in mixed vegetables and cow bone were 42.74 % and 24.74 %, respectively.

Another element, oxygen (O), has a concentration of 46.41–59.63 %. Like carbon, MW from animal waste showed the lowest concentrations of O, while VW samples had the highest value of 59.63 %. According to Fisgativa et al. (2017), there was 38.9 % O in the FW of French restaurants. One of the most essential nutrients for the growth of microorganisms and plants is nitrogen (N), a component of vitamins and amino acids. The findings showed that the highest N content was found in MW samples (10.77 %), followed by RW, BW, and VW, in that order.

As previously mentioned, meat and bone fractions with a high protein level, which suggests a high N content, can be abundant in MW samples. Even though VW samples had about double the protein level of BW ones, there was no discernible difference in their N content (p < 0.05). Prior research characterizing restaurant waste revealed that the RFW had a low N content, with values of less than 4 % (Carmona-Cabello et al., 2020; Fisgativa et al., 2017; Salimi et al., 2021). One measure utilized as an indicator in valuation procedures is the C/N ratio. It is employed in bioprocesses as a reference value and biological material degradation indicator. The degree of decomposition and the biomass's potential to self-degrade inside the FW recovery chain are two factors that may be useful in determining a biomaterial's biodegradability (Carmona-Cabello et al., 2020). The study found that the lowest C/N ratio (2.43) from MW samples (highest elemental N and most negligible elemental C value) was due to the high concentration of meat and bones and a limited amount of carbohydrates. The C/N ratios of the VW and BW samples were comparable (11.66 and 13.06, respectively), but their nutritional profiles were very dissimilar. Starch concentrations were higher in BW (61.09 % w/w, dwt), and VW samples were more prosperous in fibers (38.86 % w/w, dwt). Additionally, RW samples had a C/N ratio of 6.04, nearly half that of BW and RW samples. Fisgativa et al. (2017) recorded a C/N ratio of 22.4 for RFW, which was higher than typologically sorted RFW in the present study.

Phosphate (P) is essential for plants as it gathers solar energy and uses it to power photosynthesis. Plant waste contains a higher concentration of P (VW: 18.32 %, FRW: 28.21 %) than animal waste (cow bone: 2.38 %, chicken bone: 11.05 %), according to Islam et al. (2023). Our findings, however, demonstrated that the P content in MW samples (3.04 %) was nearly double that of plant-based waste (BW: 1.78 %, VW: 1.60 %, RW: 0.97 %).

Another crucial macronutrient in FW is potassium (K), which is essential to plants' photosynthesis. The K content of all samples was lower than 1 %, with the highest value provided by VW samples (0.73 %). Salimi et al. (2021) reported a high K content in the RFW representing 4.47 %, while Carmona-Cabello et al. (2020) found a K content of 0.26 % in the RFW similar to our findings. The Ca concentration of plant-based waste ranged from 0.03 % to 0.16 %, whereas the Ca content of MW samples was high (8.21 %) because of CaCO₃. Salimi et al. (2021) and (Carmona-Cabello et al., 2020) reported that Ca content in the RFW was 4.59 % and 0.11 %, respectively.

## Optimization of MAH

### Screening the significant variables on RSC

PBD are designed to identify the most significant factors affecting a process with a minimal number of experimental runs. This approach was particularly useful for screening a large number of variables quickly to determine which ones have the most impact on RFW hydrolysis. To determine the significant factors between the eight variables (solvent/waste ratio, mixing speed, solvent type, temperature, volume, microwave power, microwave heating time, and solvent concentration) affecting the RSC of the four different hydrolysates, 12 trials were used in PBD. The effects of these variables on the responses and significant levels are displayed in Table 1. The results of the PBD analysis showed that the RSC of hydrolysates was greatly affected by MAH. The ranges of the RSC for BWH, RWH, VWH, and MWH were 29.55 to 119.84 g/L, 46.13 to 86.78 g/L, 25.20 to 64.21 g/L, and 3.26 to 18.45 g/L, respectively. Prasertsilp et al. (2023) also revealed that the total fermentable sugar content of cassava pulp hydrolysates after different MAH conditions ranged from 0.7 to 88.1 g/L. This variation demonstrated the significance of MAH modification in improving the FW's carbohydrate hydrolysis. Significant variables were those having confidence intervals greater than 95 %. According to the results of ANOVA (Table 2), microwave power, temperature, microwave heating time, and solvent concentration were recognized as significant factors affecting the RSC of the hydrolysates under study.

### Optimization of significant variables

To examine the effects of significant variables under different operational conditions involved in MAH (temperature, microwave time, solvent concentration, and microwave power) on the RSC of the typologically sorted RFWs, RSM was used in conjunction with CCD. Three replicates were used for each of the 30 runs that were done to guarantee the accuracy of the findings. Table 3 presents the design matrices for the CCD and the actual experimental results of the responses. The RSC of BWH, RWH, VWH, and MWH varied from 29.55 to 119.84, 46.13 to 86.78, 25.20 to 64.21, and 3.26 to 18.45 g/L, respectively. The experimental data was analyzed using multiple regression analysis. Second-order polynomial equations were developed for a quadratic polynomial regression model:

$$ \begin{aligned} \text{RSC (BWH)} = 114.54 &+ 15.48X_1 + 14.11X_2 + 3.24X_3 \\ &+ 4.44X_4 - 7.77X_1^2 - 7.78X_2^2 - 13.39X_3^2 - 7.72X_4^2 \\ &- 0.32X_1X_2 - 1.37X_1X_3 - 6.21X_1X_4 - 4.89X_2X_3 \\ &- 2.34X_2X_4 + 0.43X_3X_4 \end{aligned} \quad (6) $$

$$ \begin{aligned} \text{RSC (RWH)} = 89.46 &+ 5.72X_1 - 0.86X_2 - 4.48X_3 + 2.21X_4 - 1.99X_1^2 \\ &- 3.05X_2^2 - 8.71X_3^2 - 1.60X_4^2 - 0.28X_1X_2 + 0.40X_1X_3 \\ &- 0.52X_1X_4 - 3.47X_2X_3 - 0.46X_2X_4 + 0.35X_3X_4 \end{aligned} \quad (7) $$

$$ \begin{aligned} \text{RSC (VWH)} = 65.17 &- 3.37X_1 + 4.80X_2 + 3.33X_3 + 3.95X_4 - 6.38X_1^2 \\ &+ 0.37X_2^2 - 8.32X_3^2 - 3.94X_4^2 - 1.27X_1X_2 + 0.23X_1X_3 \\ &- 0.91X_1X_4 - 3.59X_2X_3 - 4.73X_2X_4 + 1.94X_3X_4 \end{aligned} \quad (8) $$

$$ \begin{aligned} \text{RSC (MWH)} = 17.60 &+ 1.34X_1 + 3.59X_2 + 1.43X_3 + 2.65X_4 - 0.88X_1^2 \\ &- 1.26X_2^2 - 0.67X_3^2 - 1.25X_4^2 - 0.31X_1X_2 + 0.03X_1X_3 \\ &+ 0.31X_1X_4 + 0.09X_2X_3 - 1.02X_2X_4 + 0.23X_3X_4 \end{aligned} \quad (9) $$

Each quadratic model's significance was ascertained using the ANOVA (Table 4). The models were shown to be significant by the low p-values (<0.0001). In this regard, F-values of 28.79–59.81 imply that the model is significant. The data showed a reasonably good agreement when the
<table><caption>**Table 1**
The Plackett Burman design (PBD) variables with RSC (g/L) of hydrolysates as responses.</caption><thead><tr><th rowspan="2">Run</th><th>X<sub>1</sub></th><th>X<sub>2</sub></th><th>X<sub>3</sub></th><th>X<sub>4</sub></th><th>X<sub>5</sub></th><th>X<sub>6</sub></th><th>X<sub>7</sub></th><th>X<sub>8</sub></th><th colspan="4">RSC (g/L)</th></tr><tr><th>(mL/g)</th><th>(rpm)</th><th></th><th>(°C)</th><th>(mL)</th><th>(W)</th><th>(min)</th><th>(%)</th><th>BWH</th><th>RWH</th><th>VWH</th><th>MWH</th></tr></thead><tbody><tr><td>1</td><td>-1</td><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>74.55</td><td>65.28</td><td>43.50</td><td>6.25</td></tr><tr><td>2</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>-1</td><td>62.48</td><td>54.46</td><td>40.17</td><td>5.64</td></tr><tr><td>3</td><td>1</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>81.39</td><td>71.35</td><td>45.62</td><td>6.58</td></tr><tr><td>4</td><td>1</td><td>-1</td><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>1</td><td>1</td><td>40.55</td><td>31.55</td><td>23.16</td><td>2.69</td></tr><tr><td>5</td><td>-1</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>45.90</td><td>37.29</td><td>30.75</td><td>3.90</td></tr><tr><td>6</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>50.96</td><td>42.73</td><td>33.18</td><td>4.21</td></tr><tr><td>7</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>98.26</td><td>88.36</td><td>54.11</td><td>9.18</td></tr><tr><td>8</td><td>-1</td><td>-1</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>116.60</td><td>104.16</td><td>68.01</td><td>12.38</td></tr><tr><td>9</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>-1</td><td>90.03</td><td>80.19</td><td>49.33</td><td>7.28</td></tr><tr><td>10</td><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>-1</td><td>34.41</td><td>22.42</td><td>26.10</td><td>3.44</td></tr><tr><td>11</td><td>-1</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>54.92</td><td>50.11</td><td>35.41</td><td>5.08</td></tr><tr><td>12</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>1</td><td>106.47</td><td>92.20</td><td>57.20</td><td>10.11</td></tr></tbody></table>

All experiments were carried out in triplicate and expressed as mean values ± SD.

experimental findings were compared to the values predicted by the models. Indicating that the models could account for at least 95.0 % of the response's variability. The correlation between the response and the independent factors examined by the R² was > 95.0 % for all responses. Additionally, the lack of fit test was used to confirm the model's validity; insignificant p-values greater than 0.05 signify that the quadratic models could fit the RSC of various RFW samples with accuracy. The p-value for RSC models was insignificant, with the values of 0.07–0.45. The p-values are also employed to examine the importance of every coefficient, potentially revealing patterns of interaction between the variables. P-values with smaller values suggest that the associated coefficient is more significant. All linear and quadratic terms were significant except X₂² for VWH samples. X₁*X₄ was the sole important interaction term in the RWH model. X₁*X₄ and X₂*X₃ were the key interaction factors influencing RSC in the BWH example. RSC of VWH was shown to be significantly influenced by X₂*X₃, X₂*X₄, and X₃*X₄. Lastly, there were notable interaction effects between X₂*X₃ and X₂*X₄ and RSC of MWH samples. The linear term of temperature (X₁) had the most considerable impact on the RSC of BWH, followed by X₃, X₂, X₂, X₂, and X₂² while, the most significant effect for the RSC of RWH samples, sequenced as: X₂, X₁, X₃, X₂, X₂X₃, and X₂. In the case of VWH samples, the quadratic terms of solvent concentration (X₃) and temperature (X₂) had the most significant effect, followed by X₂, X₄, X₄, X₂X₄, and X₃. In contrast, linear terms had the most significant effect on the RSC of MWH samples in the following order: X₂, X₄, X₃, and X₁.

### Effect of MAH process parameters on RSC

Polysaccharides are partially broken down by acid hydrolysis to produce soluble lower molecular weight polymers, oligosaccharides, and monosaccharides (Prasertsilp et al., 2023). Temperature, microwave power, acid concentration, and microwave heating time were important parameters that affect the rate and selectivity of polysaccharides hydrolysis, as demonstrated by PBD in previous sections. The maximum CR of 64.65 % (119.84 g/L) was obtained for BWH using 2.0 % v/v HCl at 300 W and 80 °C for 15 min. The highest CR for RWH (57.14 %, 94.97 g/L) was achieved using 2.0 % v/v HCl at 300 W and 80 °C for 15 min. In the case of VWH, the highest CR of 49.27 % (76.01 g/L) was found when temperature, solvent concentration, and microwave heating time remained at medium levels (0) while microwave power was at + 2 level (500 W). Finally, hydrolysis under extreme conditions (all variables at + 1 level) produced a maximum CR of 41.20 % (10.70 g/L) for MWH samples. As can be seen, the maximum RSC and CR were observed for BWH samples. The effect of acidic hydrolysis on typologically sorted RFW varies due to variations in their carbohydrate composition and structural complexities. Generally, BW, primarily composed of starches (61.09 %, w/w) and simpler sugars, is more susceptible to acidic hydrolysis than RW, VW, and MW. This susceptibility can be attributed to the relatively higher content of accessible

carbohydrates in BW, facilitating more straightforward hydrolysis reactions. RW, although also containing starches (50.89 %, w/w), may exhibit slightly lower hydrolysis rates due to potential modifications during cooking processes. Conversely, VW, contains a higher proportion of complex polysaccharides like fibers (38.86 %, w/w) which are more resistant to hydrolysis and require higher energy input to break down into simpler sugars. Additionally, MW are characterized by diverse protein matrices (40.71 %, w/w), and exhibited comparatively slower hydrolysis rates due to the presence of more resistant bonds and complex structures (Vavouraki et al., 2013). Ma et al. (2019) reported that a CR of 92.5 % was achieved for the refectory's FW using diluted H₂SO₄ at 120 °C for 60 min which was higher than the results of this study. Still, it's noteworthy to consider the low temperature and short time were used in the present study by employing MAH.

### Microwave power and time.
To investigate the relationship between independent variables, interaction plots (Fig. S1-4) and contour plots (Fig. S5-8) described by the regression models were generated. Interaction plots were utilized to show and interpret the experimental data's interaction effect. Contour plots on the other hand provide valuable insights into the nature of this relationship by depicting contours of constant response levels on a two-dimensional plane (Shangdiar et al., 2023).

The results indicate that higher microwave powers generally lead to increased hydrolysis of carbohydrates in the RFW samples. As shown in Figs. S1-8, at a constant acid concentration of 1.5 % v/v HCl, a temperature of 80 °C, and a microwave heating time of 10 min, increasing the microwave power from 200 W to 400 W led to an increase in the RSC of BWH, VWH, and MWH samples from 78.15 g/L to 111.04 g/L (1.46 fold), 47.83 g/L to 70.30 g/L (1.47 fold), and 3.12 g/L to 8.53 g/L (2.73 fold), respectively. This trend is attributed to three phenomena: (1) rapid and uniform heating of the reaction mixture by microwave, leading to higher temperatures than conventional heating methods. This rapid heating promotes faster reaction kinetics, including the cleavage of glycosidic bonds in carbohydrates, resulting in accelerated hydrolysis rates; (2) enhanced mass transfer within the reaction mixture by promoting convection and diffusion of reactants and products. This improved mass transfer facilitates better contact between the acidic solvent and the carbohydrate in the RFWs, increasing the hydrolysis efficiency; (3) selective heating of polar molecules, such as acidic solvents, within the reaction mixture. This selective heating ensures that the energy is efficiently transferred to the solvent and reactants, leading to localized heating at the reaction site and minimizing energy losses (Shangdiar et al., 2023). According to Prasertsilp et al. (2023), fermentable sugar content in cassava hydrolysates increased from 39.0 g/L to 88.1 g/L with an increase in microwave power from 600 to 800 W. However, at longer microwave heating times (20 min), a weaker effect of the power was observed on the derived sugars as the curve's
<table><caption>**Table 2**
Analysis of variance (ANOVA) for Plackette-Burman design (PBD).</caption><thead><tr><th rowspan="2">Factors</th><th rowspan="2">Code</th><th rowspan="2">Low level (-1)</th><th rowspan="2">High level (+1)</th><th colspan="2">BWH</th><th colspan="2">RWH</th><th colspan="2">VWH</th><th colspan="2">MWH</th><th colspan="2">R<sup>2</sup></th></tr><tr><th>Effect</th><th>Coef.</th><th>P-value</th><th>Effect</th><th>Coef.</th><th>P-value</th><th>Effect</th><th>Coef.</th><th>P-value</th><th>Effect</th><th>Coef.</th><th>P-value</th></tr></thead><tbody><tr><td>Solvent/waste ratio (mL/g)</td><td>X<sub>1</sub></td><td>5</td><td>20</td><td>-1.57</td><td>-0.78</td><td>0.68<sup>ns</sup></td><td>-3.24</td><td>-1.62</td><td>0.425<sup>ns</sup></td><td>-2.303</td><td>-1.15</td><td>0.28<sup>ns</sup></td><td>-0.24</td><td>-0.12</td><td>0.62<sup>ns</sup></td></tr><tr><td>Mixing speed (rpm)</td><td>X<sub>2</sub></td><td>90</td><td>180</td><td>10.85</td><td>5.43</td><td>0.05<sup>ns</sup></td><td>10.24</td><td>5.12</td><td>0.062<sup>ns</sup></td><td>4.43</td><td>2.22</td><td>0.08<sup>ns</sup></td><td>0.47</td><td>0.23</td><td>0.36<sup>ns</sup></td></tr><tr><td>Solvent type</td><td>X<sub>3</sub></td><td>60</td><td>HCl</td><td>-5.74</td><td>-2.87</td><td>0.20<sup>ns</sup></td><td>-3.73</td><td>-1.87</td><td>0.366<sup>ns</sup></td><td>-4.96</td><td>-2.48</td><td>0.07<sup>ns</sup></td><td>-1.22</td><td>-0.61</td><td>0.07<sup>ns</sup></td></tr><tr><td>Temperature (°C)</td><td>X<sub>4</sub></td><td>50</td><td>H<sub>2</sub>SO<sub>4</sub></td><td>50.25</td><td>25.13</td><td>0.00<sup>**</sup></td><td>49.72</td><td>24.86</td><td>0.003<sup>**</sup></td><td>25.48</td><td>12.74</td><td>0.00<sup>***</sup></td><td>5.65</td><td>2.83</td><td>0.00<sup>***</sup></td></tr><tr><td>Solvent volume (mL)</td><td>X<sub>5</sub></td><td>200</td><td>400</td><td>-10.85</td><td>-5.43</td><td>0.05<sup>ns</sup></td><td>-10.09</td><td>-5.04</td><td>0.064<sup>ns</sup></td><td>-2.40</td><td>-1.20</td><td>0.26<sup>ns</sup></td><td>-0.45</td><td>-0.23</td><td>0.37<sup>ns</sup></td></tr><tr><td>Microwave power (W)</td><td>X<sub>6</sub></td><td>10</td><td>20</td><td>22.33</td><td>11.16</td><td>0.01<sup>**</sup></td><td>20.57</td><td>10.29</td><td>0.01<sup>**</sup></td><td>11.13</td><td>5.57</td><td>0.01<sup>**</sup></td><td>2.25</td><td>1.12</td><td>0.01<sup>**</sup></td></tr><tr><td>Microwave heating time (min)</td><td>X<sub>7</sub></td><td>10</td><td>20</td><td>19.02</td><td>9.51</td><td>0.01<sup>**</sup></td><td>17.61</td><td>8.81</td><td>0.008<sup>**</sup></td><td>9.49</td><td>4.75</td><td>0.01<sup>**</sup></td><td>2.13</td><td>1.07</td><td>0.01<sup>**</sup></td></tr><tr><td>Solvent concentration (% v/v)</td><td>X<sub>8</sub></td><td>1.0</td><td>2.0</td><td>23.11</td><td>11.55</td><td>0.02<sup>**</sup></td><td>22.30</td><td>11.15</td><td>0.024<sup>**</sup></td><td>9.81</td><td>4.91</td><td>0.03<sup>**</sup></td><td>2.36</td><td>1.18</td><td>0.04<sup>*</sup></td></tr><tr><td colspan="12"></td><td>R<sup>2</sup> = 98.63, R<sup>adj</sup> = 94.98, R<sup>pre</sup> = 78.09</td></tr></tbody></table>

ns, Not significant (p > 0.05).

* Significant at (p < 0.05).

** Highly significant at (p < 0.01).

*** Extremely significant at (p < 0.001).

<caption>**Table 3**</caption>

Central composite design (CCD) design and experimental results obtained for the optimization of MAH of typologically sorted RFW.

<table><caption>**Table 4**</caption><thead><tr><th rowspan="2">Run</th><th colspan="4">Independent factors</th><th colspan="4">RSC (g/L)</th></tr><tr><th>X<sub>1</sub></th><th>X<sub>2</sub></th><th>X<sub>3</sub></th><th>X<sub>4</sub></th><th>BWH</th><th>RWH</th><th>VWH</th><th>MWH</th></tr></thead><tbody><tr><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>119.84</td><td>86.78</td><td>64.21</td><td>9.23</td></tr><tr><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td><td>68.48</td><td>46.13</td><td>37.18</td><td>9.09</td></tr><tr><td>3</td><td>-2</td><td>0</td><td>0</td><td>0</td><td>45.08</td><td>69.68</td><td>44.29</td><td>6.63</td></tr><tr><td>4</td><td>0</td><td>-2</td><td>0</td><td>0</td><td>47.34</td><td>81.47</td><td>55.88</td><td>3.75</td></tr><tr><td>5</td><td>0</td><td>2</td><td>0</td><td>0</td><td>115.26</td><td>74.75</td><td>76.01</td><td>10.26</td></tr><tr><td>6</td><td>0</td><td>0</td><td>-2</td><td>0</td><td>49.24</td><td>64.79</td><td>25.20</td><td>7.29</td></tr><tr><td>7</td><td>2</td><td>0</td><td>0</td><td>0</td><td>117.61</td><td>94.97</td><td>33.63</td><td>8.92</td></tr><tr><td>8</td><td>0</td><td>0</td><td>0</td><td>-2</td><td>68.87</td><td>80.30</td><td>36.55</td><td>4.63</td></tr><tr><td>9</td><td>0</td><td>0</td><td>0</td><td>2</td><td>94.21</td><td>87.47</td><td>60.87</td><td>9.45</td></tr><tr><td>10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>112.18</td><td>90.53</td><td>70.51</td><td>8.58</td></tr><tr><td>11</td><td>-1</td><td>-1</td><td>1</td><td>1</td><td>68.18</td><td>71.57</td><td>60.39</td><td>6.59</td></tr><tr><td>12</td><td>-1</td><td>1</td><td>-1</td><td>-1</td><td>71.71</td><td>76.25</td><td>58.41</td><td>6.20</td></tr><tr><td>13</td><td>-1</td><td>1</td><td>-1</td><td>1</td><td>86.86</td><td>75.98</td><td>54.16</td><td>7.39</td></tr><tr><td>14</td><td>1</td><td>1</td><td>-1</td><td>1</td><td>100.13</td><td>88.76</td><td>42.99</td><td>9.08</td></tr><tr><td>15</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>87.17</td><td>63.59</td><td>57.26</td><td>9.49</td></tr><tr><td>16</td><td>0</td><td>0</td><td>0</td><td>0</td><td>117.20</td><td>88.28</td><td>65.48</td><td>8.73</td></tr><tr><td>17</td><td>1</td><td>-1</td><td>1</td><td>-1</td><td>83.49</td><td>78.38</td><td>38.00</td><td>4.32</td></tr><tr><td>18</td><td>-1</td><td>-1</td><td>-1</td><td>1</td><td>55.69</td><td>73.70</td><td>47.23</td><td>4.21</td></tr><tr><td>19</td><td>1</td><td>1</td><td>-1</td><td>-1</td><td>117.43</td><td>83.33</td><td>52.47</td><td>6.94</td></tr><tr><td>20</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>89.06</td><td>80.72</td><td>58.72</td><td>8.24</td></tr><tr><td>21</td><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>74.99</td><td>78.11</td><td>29.33</td><td>2.41</td></tr><tr><td>22</td><td>0</td><td>0</td><td>0</td><td>0</td><td>110.17</td><td>90.29</td><td>65.51</td><td>8.95</td></tr><tr><td>23</td><td>1</td><td>-1</td><td>-1</td><td>1</td><td>75.43</td><td>82.07</td><td>34.36</td><td>6.68</td></tr><tr><td>24</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>96.84</td><td>72.41</td><td>45.90</td><td>10.70</td></tr><tr><td>25</td><td>-1</td><td>1</td><td>1</td><td>-1</td><td>65.32</td><td>56.54</td><td>57.82</td><td>7.82</td></tr><tr><td>26</td><td>0</td><td>0</td><td>0</td><td>0</td><td>116.09</td><td>89.30</td><td>65.11</td><td>8.70</td></tr><tr><td>27</td><td>0</td><td>0</td><td>0</td><td>0</td><td>111.79</td><td>91.55</td><td>60.17</td><td>8.63</td></tr><tr><td>28</td><td>-1</td><td>-1</td><td>1</td><td>-1</td><td>53.70</td><td>62.21</td><td>41.48</td><td>2.18</td></tr><tr><td>29</td><td>1</td><td>1</td><td>1</td><td>-1</td><td>107.31</td><td>68.93</td><td>46.39</td><td>8.71</td></tr><tr><td>30</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>29.55</td><td>66.26</td><td>30.95</td><td>1.63</td></tr></tbody></table>

Analysis of variance (ANOVA) for central composite design (CCD).

<table><thead><tr><th rowspan="2">Term</th><th rowspan="2">df</th><th colspan="4">Sum of Square</th></tr><tr><th>BWH</th><th>RWH</th><th>VWH</th><th>MWH</th></tr></thead><tbody><tr><td>Model</td><td>15</td><td>19619.70<sup>***</sup></td><td>3791.38<sup>***</sup></td><td>5080.90<sup>***</sup></td><td>175.04<sup>***</sup></td></tr><tr><td>X<sub>1</sub></td><td>1</td><td>5752.60<sup>***</sup></td><td>784.21<sup>***</sup></td><td>272.43<sup>***</sup></td><td>10.85<sup>***</sup></td></tr><tr><td>X<sub>2</sub></td><td>1</td><td>4774.60<sup>***</sup></td><td>17.80<sup>*</sup></td><td>552.96<sup>***</sup></td><td>77.38<sup>***</sup></td></tr><tr><td>X<sub>3</sub></td><td>1</td><td>251.90<sup>*</sup></td><td>480.88<sup>***</sup></td><td>266.80<sup>***</sup></td><td>12.22<sup>***</sup></td></tr><tr><td>X<sub>4</sub></td><td>1</td><td>473.00<sup>**</sup></td><td>117.62<sup>***</sup></td><td>374.46<sup>***</sup></td><td>42.18<sup>***</sup></td></tr><tr><td>X<sub>1</sub>X<sub>2</sub></td><td>1</td><td>1656.70<sup>***</sup></td><td>109.06<sup>***</sup></td><td>1115.73<sup>***</sup></td><td>5.29<sup>***</sup></td></tr><tr><td>X<sub>2</sub>X<sub>3</sub></td><td>1</td><td>1661.50<sup>***</sup></td><td>254.79<sup>***</sup></td><td>3.72<sup>ns</sup></td><td>10.95<sup>***</sup></td></tr><tr><td>X<sub>3</sub>X<sub>4</sub></td><td>1</td><td>4919.70<sup>***</sup></td><td>2080.99<sup>***</sup></td><td>1898.86<sup>***</sup></td><td>3.08<sup>***</sup></td></tr><tr><td>X<sub>4</sub>X<sub>5</sub></td><td>1</td><td>1635.80<sup>***</sup></td><td>70.57<sup>***</sup></td><td>425.88<sup>***</sup></td><td>10.65<sup>***</sup></td></tr><tr><td>X<sub>1</sub>X<sub>2</sub></td><td>1</td><td>1.70<sup>ns</sup></td><td>1.25<sup>ns</sup></td><td>25.65<sup>ns</sup></td><td>0.39<sup>ns</sup></td></tr><tr><td>X<sub>1</sub>X<sub>3</sub></td><td>1</td><td>29.90<sup>ns</sup></td><td>2.60<sup>ns</sup></td><td>0.84<sup>ns</sup></td><td>0.00<sup>ns</sup></td></tr><tr><td>X<sub>1</sub>X<sub>4</sub></td><td>1</td><td>617.30<sup>***</sup></td><td>4.38<sup>ns</sup></td><td>13.32<sup>ns</sup></td><td>0.38<sup>ns</sup></td></tr><tr><td>X<sub>2</sub>X<sub>3</sub></td><td>1</td><td>382.70<sup>**</sup></td><td>193.14<sup>***</sup></td><td>205.78<sup>***</sup></td><td>0.03<sup>ns</sup></td></tr><tr><td>X<sub>2</sub>X<sub>4</sub></td><td>1</td><td>87.40<sup>ns</sup></td><td>3.43<sup>ns</sup></td><td>358.34<sup>***</sup></td><td>4.20<sup>***</sup></td></tr><tr><td>X<sub>3</sub>X<sub>4</sub></td><td>1</td><td>3.10<sup>ns</sup></td><td>2.01<sup>ns</sup></td><td>60.06<sup>*</sup></td><td>0.21<sup>ns</sup></td></tr><tr><td>R<sup>2</sup></td><td></td><td>97.66</td><td>98.46</td><td>96.86</td><td>98.07</td></tr><tr><td>Adj R<sup>2</sup></td><td></td><td>95.15</td><td>96.82</td><td>93.50</td><td>96.01</td></tr><tr><td>Pre R<sup>2</sup></td><td></td><td>85.72</td><td>92.03</td><td>83.73</td><td>89.41</td></tr><tr><td>Model (F-value)</td><td></td><td>38.90</td><td>59.81</td><td>28.79</td><td>47.51</td></tr><tr><td>Model (P-value)</td><td></td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>Lack of fit (F-value)</td><td></td><td>2.57</td><td>1.44</td><td>1.24</td><td>4.76</td></tr><tr><td>Lack of fit (P-value)</td><td></td><td>0.19</td><td>0.38</td><td>0.45</td><td>0.07</td></tr></tbody></table>

* Significant at (p < 0.05).

** Highly significant at (p < 0.01).

*** Extremely significant at (p < 0.001).

ns Not significant (p > 0.05).

steepness gradually decreased.

The RSC of BWH and MWH samples increased from 3.91 g/L to 8.53 g/L (2.18 fold) and 7.58 g/L to 10.15 g/L (1.33 fold), respectively, by
increasing microwave power from 200 to 400 W, while no significant increase was observed for VWH samples as their RSC remained constant. The increase in microwave power during a shorter time may have provided an energy input that effectively enhanced carbohydrate breakdown and increased RSC in VWH. However, during the 20-min treatment, the hydrolysis reaction may have already reached saturation or plateaued, and a further increase in microwave power may not significantly improve hydrolysis efficiency.

In the case of RWH samples, excessively high power levels and prolonged microwave heating times led to a decrease in the RSC of RWH samples. As microwave power was increased from 200 W to 400 W with the remaining other variables constant (X₁: 80 °C, X₃: 1.5 %, X₄: 20 min), the RSC of RWH samples declined a little, from 88.34 g/L to 85.69 g/L (1.03 fold decrease). Extended exposure to microwave Microwave can lead to the degradation of hydrolyzed carbohydrate products, the formation of by-products that are less susceptible to further hydrolysis, and the formation of inhibitory compounds (Maillard reaction and caramelization products) (Gao et al., 2021; Zeng et al., 2017). In contrast, the shorter treatment duration (10-min) may not provide sufficient time for these effects to manifest, resulting in no significant change in the RSC of RWH samples. Longer treatments showed a similar tendency. Following a 20-min treatment, the RSC of the BWH, RWH, and MWH samples showed a slight decrease. In the investigation conducted by Hermiati et al. (2012) on microwave-assisted hydrolysis of cassava pulp, it was observed that the glucose yield remained notably low during the initial 7.5 min of Microwave. However, a substantial increase in glucose yield was evident after 10 min of Microwave.

**Temperature.** Temperature plays a crucial role in the acidic hydrolysis of carbohydrates by influencing reaction kinetics, activation energy, acid catalyst stability, product distribution, and energy consumption, as highlighted in several studies (Fan et al., 2014; He et al., 2012; Hermiati et al., 2012; Prasertsilp et al., 2023; Vavouraki et al., 2013; Zeng et al., 2017). The effect of temperature on the carbohydrate hydrolysis of different RFW samples is illustrated in Figs. S1-S4 (Interaction plots) and Figs. S5-S8 (contour plots). It was discovered that when the temperature rose from 60 °C to 100 °C (X₂: 300 W, X₃: 1.5 %), the RSC in the BWH, RWH, and MWH samples steadily increased from 69.07 g/L to 100.69 g/L (1.46 fold), 77.40 g/L to 89.88 g/L (1.16 fold), and 5.89 g/L to 6.93 g/L (1.18 fold), respectively. In fact, under normal conditions, polysaccharides maintain a stable crystalline structure. However, at elevated temperatures, the monosaccharide units within the polysaccharide predominantly adopt an open-chain conformation, which is less stable compared to the ring form. Consequently, this structural change renders the polysaccharide more susceptible to hydrolysis. With increasing temperature, molecules exhibit higher kinetic energy, leading to an augmented collision frequency between the substrate and hydronium ions. H⁺ and OH⁻ facilitate the hydrolysis of carbohydrates into monosaccharides (Hafid et al., 2017).

Longer treatment times showed a similar tendency. Following a 20-min treatment, the steepness of RSC versus temperature showed a slight decrease. As previous research indicates, elevated temperatures can induce side reactions like the Maillard reaction, resulting in a reduction in the levels of reducing sugars (Fan et al., 2014; Vavouraki et al., 2013). At higher temperatures, prolonged exposure to microwave Microwave may cause the breakdown of sugars into smaller molecules or the formation of non-reducing compounds through thermal degradation pathways. In Fan et al. (2014) study, microwave Microwave of palm kernel cake with dilute sulfuric acid showed an initial rise in monosaccharide yield as temperature increased from 140 to 165 °C, followed by a subsequent decline with further temperature elevation. In contrast, a 1.10-fold decrease in soluble sugars was observed for VWH samples by temperature shift to 100 °C. Additional treatment for 20 min resulted in a decrease of RSC from 63.07 g/L to 54.51 g/L (1.15 fold). VW contains a higher proportion of complex carbohydrates such as cellulose,

hemicellulose, and pectin compared to other types of waste. These complex carbohydrates are more resistant to hydrolysis and require higher energy input to break down into simpler sugars. Therefore, the increase in temperature may not be sufficient to effectively break down these complex carbohydrates in VW, resulting in a lower release of reducing sugars. Additionally, the formation of non-reducing compounds or by-products may result in the decrease of RSC in VWH (Vavouraki et al., 2013).

**Solvent concentration.** Biomass pretreatment primarily entails hydrolysis using diluted acids to extract fermentable sugars. H₂SO₄ pretreatment is commonly preferred due to its reduced risk of degradation and corrosion within equipment. However, HCl has also been utilized, despite its corrosive nature and associated application limitations. Additionally, HCl is favored for its easier recovery compared to H₂SO₄. It is frequently employed for completely hydrolysis plant-derived carbohydrates into simple reducing sugars without detrimental effects. According to Vavouraki et al. (2013), at 50 °C across various concentrations and reaction times, HCl exhibited comparable hydrolysis efficacy to H₂SO₄. Nevertheless, at 100 °C and concentrations of 0.7 % and 1.5 %, HCl demonstrated superior effectiveness over H₂SO₄.

Acid concentration, aside from temperature, holds considerable importance in carbohydrate hydrolysis. Hafid et al. (2017) noted that under equivalent reaction temperatures, hydrolysis efficiency was markedly influenced by the acid type and concentration, with a more significant effect observed compared to variations in reaction time. Conversely, Vavouraki et al. (2013) stated that temperature exerted the most significant influence on carbohydrate hydrolysis, compared to acid concentration and reaction duration. As illustrated in Figs. S1–8, as the acid concentration increased progressively to 1.5 % v/v under constant conditions (X₁: 80 °C, X₂: 300 W, and X₄: 15 min), there was a corresponding improvement in the selectivity towards sugars, consequently enhancing the RSC of all RFW samples. BHW, RHW, VWH, and MWH samples exhibited respective increases in fermentable sugar content of 18.18 g/L (1.18 fold), 4.08 g/L (1.05 fold), 12.00 g/L (1.22 fold), and 1.20 g/L (1.15 fold) upon altering the HCl concentration to 1.5 %. The elevation in sugar levels associated with rising acid concentration may be attributed to heightened charge perturbation within the boundary layer. The augmented presence of hydronium ions induces ionic disturbances, thereby facilitating the sugars to transition into a “released” state Fan et al. (2014). Similar findings were reported by Vavouraki et al. (2013), where the increase of HCl concentration from 0.7 % to 3.0 % had a positive effect on the hydrolysis of KW’s carbohydrate. However, increasing the HCl concentration to 3.0 % introduced an adverse impact on the selectivity of sugars resulting in a decrement of RSC. By increasing the HCl concentration to 3.0 %, the RSC of the BWH, RWH, and VWH samples declined about 11.7 g/L (1.11 fold), 14.27 g/L (1.19 fold), and 5.34 g/L (1.09 fold), respectively. This could be attributed to the degradation or caramelization of sugars and the formation of Hydroxymethylfurfural (HMF). Fan et al. (2014) found that the formation of HMF is favorable at an acid concentration higher than 0.5 N. Prasertsilp et al. (2023) also reported that for cassava pulp hydrolysis, the optimal H₂SO₄ concentration was 1.5 %. However, the total fermentable sugars declined from 88.1 g/L to 80.8 g/L as the concentration of H₂SO₄ increased to 3.0 %.

## Validation of optimum conditions

The typologically sorted RFW underwent a MAH process to raise the fermentable sugar content in the final hydrolysates to aid in its ensuing biochemical conversion in the biological processes. Consequently, the main goal of response optimization was to develop regression models to determine the ideal values for the variables to provide the highest possible RSC. As previous studies stated the confidence interval was a helpful tool for assessing the importance of the data, a 95 % confidence level of fit was developed to ensure that any variables in the model were
accurate (Shangdiar et al., 2023). The optimum values of the studied variables in the MAH process were discovered as temperature of 96.0 °C, microwave power of 340 W, solvent concentration of 1.45 %, and microwave heating time of 11.1 min. The average (n = 3) RSC obtained under optimized conditions for BWH, RWH, VWH, and MWH was 125.18 ± 2.14, 92.11 ± 2.34, 57.24 ± 1.95, and 9.48 ± 1.02 g/L, respectively. Regression models predicted values of 126.91, 90.41, 58.64, and 10.18 g/L for the responses above, which agrees reasonably well with the experiment outcomes. According to the study conducted by Zeng et al. (2017), the most favorable parameters for the acidic hydrolysis of food waste were 120 °C temperature, a duration of 60 min, and an H₂SO₄ concentration of 1.5 % (v/v). Fan et al. (2020) reported that treatment at 130 °C for 60 min using distilled water with a solid-liquid ratio of 1:4 was the optimum condition for hydrolysis of the canteen's FW. A study by Hafid et al. (2017) found that using 1.0 % H₂SO₄ at 100 °C for 180 min resulted in the best hydrolysis condition for food court waste. The hydrolysates obtained under the optimal conditions of MAH were selected for monosaccharide composition, FT-IR, and biochemical analysis (Fig. 3).

## Monosaccharides composition

HPLC analysis was performed for the hydrolysates obtained by MAH of typologically sorted RFW (Fig. 4). Except for MWH; all hydrolysates exhibited a substantial content of fermentable sugars. BWH displayed the highest RSC (125.18 ± 2.14 g/L), followed by RWH (92.11 ± 2.34 g/L) and VWH (57.24 ± 1.95 g/L). Mannose, rhamnose, glucuronic acid, galacturonic acid, fructose, glucose, xylose, and arabinose were detected in all samples. However, Galactose was only found in MWH and RWH. Glucose was the main constituent of BWH (45.72 %, 47.63 mg/g) and VWH (50.17 %, 27.42 mg/g). Arabinose (16.76 %, 13.77 mg/g) constitutes the primary monosaccharide within the RWH, with glucose (15.82 %, 13.00 mg/g) and xylose (15.66 %, 12.87 mg/g) following as the next most abundant. In the case of MWH, rhamnose (21.38 %, 2.17 mg/g) and arabinose (19.02 %, 1.93 mg/g) comprised the main monosaccharides. Hafid et al. (2017) also reported that FWH obtained by acid-enzymatic pretreatment mainly consisted of glucose (98.13 g/L), fructose (1.74 g/L), sucrose (0.69 g/L) and maltose (2.84 g/L). The total quantification of glucose and other monosaccharides within any of the hydrolysates was lower than the determined RSC. This disparity may arise from the presence of disaccharides, the quantification of which was not feasible. The finding was supported by Vavouraki et al., (2013) who

reported the incompatibility of HPLC analysis with total soluble sugar content due identification of a di-saccharide peak.

## Biochemical characteristics of hydrolysates

The hydrolyzed RFWs underwent extensive physicochemical evaluation. Table 5 presents hydrolysate characterization results. The findings suggest that all hydrolysates are suitable for utilization as culture media in bioprocesses such as fermentation due to their high nutrient content. The pH of all hydrolysates fell within the acidic range (pH<2.74), characteristic of substrates acquired through acidic hydrolysis, necessitating alkalization before the bioprocesses. The evaluation of wastewater is significantly influenced by BOD and COD, which reflect the requisite oxygen demand. The high COD and BOD values are ascribed to starchy biomass (BWH and RWH), lignocellulosic biomass (VWH), and a high protein content in MWH, indicating significant organic load to be degraded. The highest BOD (49520 mg/L) and COD (79849 mg/L) levels were observed in BWH and RWH, respectively. In contrast, MWH represented the lowest BOD (14128 mg/L) and COD (23873 mg/L). The low COD/BOD ratio (<2.08) indicated the high biodegradability of all hydrolysates. Nitrate, nitrite, ammonium, and organic nitrogen compounds (such as amino acids, urea, and proteins) represent the N forms present in wastewater. FAN as the organic N was the main fraction of the N in the derived hydrolysates. The bioavailability and degradability of organic N is confirmed by previous studies as it induces algal and bacteria growth (Chen et al., 2021). MWH represented the highest FAN content (18.42 g/L) due to the high protein content in MW samples. Amidst these N compounds, ammonium emerges as the energetically favored N source, prioritized when available, whereas nitrate and nitrite represent the least prevalent form of inorganic N. The VWH samples exhibited the highest concentrations of nitrate (199.22 mg/L) and TAN (426.60 mg/L), which may be attributed to the application of fertilizers utilized in the cultivation of vegetables (Ma et al., 2020). Under acidic conditions, the conversion of organic N into ammonium ions (NH₄⁺) is favored over nitrate (NO₃⁻) formation. It's noteworthy that acidic condition results in ammonia (NH₃) conversion to ammonium ions (NH₄⁺) as evidenced by our results (Table 5). The TAN concentration in MWH and BWH samples exceeded that of nitrate. However, RWH and VWH displayed contrasting results. The element P is necessary for plant, animal, and microorganism growth. MWH showed a very high P concentration (4523.75 mg/L), which may have been brought on by the bones' CaPO₄ content. VWH also had considerable P content that resulted from crop

<figure><img src="image_5.png" /><figcaption>Fig. 3. Hydrolysates derived from typologically sorted RFW under optimal conditions of MAH.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 4. HPLC chromatograms of the monosaccharide compositions in the hydrolysates obtained by MAH. (A) BWH; (B) RWH; (C) VWH; (D) MWH.</figcaption></figure>
<table><caption>**Table 5**
Summary of biochemical analysis of studied hydrolysates.</caption><thead><tr><th rowspan="2">Parameter</th><th rowspan="2">Unit</th><th colspan="4">Value</th></tr><tr><th>BWH</th><th>RWH</th><th>VWH</th><th>MWH</th></tr></thead><tbody><tr><td>RSC</td><td>g/L</td><td>125.18 ±<br>2.14<sup>a</sup></td><td>92.11 ±<br>2.34<sup>b</sup></td><td>57.24 ±<br>1.95<sup>c</sup></td><td>9.48 ±<br>1.02<sup>d</sup></td></tr><tr><td>CR</td><td>%</td><td>67.54 ±<br>1.28</td><td>55.42 ±<br>1.89</td><td>37.10 ±<br>1.34</td><td>36.50 ±<br>2.37</td></tr><tr><td>FAN</td><td>g/L</td><td>2.34 ±<br>0.05<sup>c</sup></td><td>6.32 ±<br>0.17<sup>b</sup></td><td>7.39 ±<br>0.04<sup>b</sup></td><td>18.42 ±<br>0.13<sup>a</sup></td></tr><tr><td>TAN</td><td>mg/L</td><td>198.83 ±<br>1.35<sup>a</sup></td><td>63.60 ±<br>0.61<sup>b</sup></td><td>199.22 ±<br>1.47<sup>a</sup></td><td>196.30 ±<br>0.95<sup>a</sup></td></tr><tr><td>Nitrate</td><td>mg/L</td><td>177.43 ±<br>1.20<sup>c</sup></td><td>379.53 ±<br>2.89<sup>b</sup></td><td>426.60 ±<br>1.35<sup>a</sup></td><td>67.17 ±<br>1.12<sup>d</sup></td></tr><tr><td>Nitrite</td><td>mg/L</td><td>2.14 ±<br>0.03<sup>b</sup></td><td>0.79 ±<br>0.03<sup>c</sup></td><td>3.25 ±<br>0.02<sup>a</sup></td><td>2.29 ±<br>0.04<sup>b</sup></td></tr><tr><td>Ammonia</td><td>mg/L</td><td>0.00 ±<br>0.00</td><td>0.002 ±<br>0.00<sup>a</sup></td><td>0.00 ±<br>0.00</td><td>0.007 ±<br>0.001<sup>a</sup></td></tr><tr><td>Ammonium</td><td>mg/L</td><td>198.60 ±<br>0.72<sup>a</sup></td><td>63.59 ±<br>1.64<sup>b</sup></td><td>199.21 ±<br>1.03<sup>a</sup></td><td>196.29 ±<br>1.17<sup>a</sup></td></tr><tr><td>Phosphate</td><td>mg/L</td><td>151.18 ±<br>1.39<sup>c</sup></td><td>77.10 ±<br>1.55<sup>d</sup></td><td>469.40 ±<br>1.40<sup>b</sup></td><td>4523.75 ±<br>3.26<sup>a</sup></td></tr><tr><td>Sulfate</td><td>mg/L</td><td>500.13 ±<br>1.34<sup>b</sup></td><td>193.91 ±<br>1.48<sup>c</sup></td><td>1186.15 ±<br>1.57<sup>a</sup></td><td>497.43 ±<br>0.83<sup>b</sup></td></tr><tr><td>TDS</td><td>mg/L</td><td>35386 ±<br>10<sup>b</sup></td><td>42025 ± 5<sup>a</sup></td><td>35556 ±<br>10<sup>b</sup></td><td>32845 ± 5<sup>c</sup></td></tr><tr><td>TSS</td><td>mg/L</td><td>1191.67 ±<br>1.53<sup>c</sup></td><td>1352.00 ±<br>2.00<sup>b</sup></td><td>1621.33 ±<br>2.31<sup>a</sup></td><td>1062.33 ±<br>2.52<sup>d</sup></td></tr><tr><td>BOD</td><td>mg/L</td><td>49520 ±<br>23<sup>a</sup></td><td>46050 ±<br>16<sup>b</sup></td><td>28221 ±<br>21<sup>c</sup></td><td>14128 ±<br>29<sup>d</sup></td></tr><tr><td>COD</td><td>mg/L</td><td>68427 ±<br>27<sup>b</sup></td><td>79849 ±<br>25<sup>a</sup></td><td>58753 ±<br>27<sup>c</sup></td><td>23873 ±<br>24<sup>d</sup></td></tr><tr><td>COD/BOD<br>ratio</td><td>L</td><td>1.38</td><td>1.73</td><td>2.08</td><td>1.70</td></tr><tr><td>EC</td><td>mS/cm</td><td>100.5 ±<br>0.0<sup>b</sup></td><td>116.6 ±<br>0.1<sup>a</sup></td><td>84.5 ± 0.1<sup>c</sup></td><td>31.2 ± 0.1<sup>d</sup></td></tr><tr><td>pH</td><td>–</td><td>0.84 ±<br>0.04<sup>b</sup></td><td>0.69 ±<br>0.01<sup>c</sup></td><td>0.80 ±<br>0.04<sup>b</sup></td><td>2.74 ±<br>0.03<sup>a</sup></td></tr></tbody></table>

All experiments were carried out in triplicate and expressed as mean values ± SD.

Different letters between the columns show statistically significant differences (p ≤ 0.05).

uptakes of fertilizer and pesticides (Ma et al., 2020). The most prevalent type of sulfur in wastewater is sulfate. Sulfur is a secondary macronutrient that is necessary for the operation of chloroplasts in plants. VWH represented the highest sulfate content (1186 mg/L), which could be due to the use of ammonium sulfate fertilizers for crop production. High sulfate content was also detected in BWH (500.13 mg/L) indicating the possible use of calcium sulfate or ammonium sulfate as a dough conditioner and strengthener. The EC represents the capacity of water to pass electric current, which is affected by dissolved minerals. RWH had the highest EC (116.6 mS/cm), which might have resulted from the rice being cooked with table salt added.

## FT-IR spectra

FTIR spectroscopy was used to find the chemical structure of the typologically sorted RFWs (Fig. 5A) and their hydrolysates (Fig. 5B). In all FTIR spectra, a broad and strong peak emerged between 3400 cm⁻¹ to 3498 cm⁻¹ were attributed to the O–H stretching vibrations, indicating the presence of carboxylic acids, alcohols, and phenols (Islam et al., 2023; Opatokun et al., 2016). Interestingly, VW exhibited the highest intensity of this peak, followed by BW, MW, and RW, in descending order. This variation suggests differences in the composition and abundance of compounds containing hydroxyl functional groups among the RFW samples. According to the chemical analysis (see Fig. 3A), VW, BW, and RW, known for their high carbohydrate content, likely contributed to the intense peak. VW may contain a lower proportion of carbohydrates compared to BW and RW. Still, the presence of other functional groups like phenols led to a more intense peak despite

the lower carbohydrate content. On the other hand, MW showed a slightly higher intensity compared to RW, which could be due to the possible collagens. Two distinct, narrow peaks were detected at approximately 2920 cm⁻¹ and 2850 cm⁻¹, attributed to the stretching vibration of C-H bonds within the aliphatic chain, representing the primary structural component of lignocellulosic compounds. A narrow peak, observed at 1740 cm⁻¹, is related to C=O bonds of fatty acids, phospholipids, and hemicelluloses within the samples (Islam et al., 2023). MW exhibited the most pronounced intensity among the samples, attributable to its elevated lipid content compared to the other waste materials. In all waste samples, a consistent narrow to medium peak emerged within the range of 1630 cm⁻¹ to 1662 cm⁻¹, suggesting the presence of N-H vibrations associated with amino acids and proteins. Likewise, within the spectrum of MW, a sharp and intense peak observed at 1420 cm⁻¹ implies the presence of S=O stretching vibrations, indicating the high protein content of MW (40.71 % w/w, dwt). The minor band observed at 1380 cm⁻¹ corresponds to stretching vibrations of O-H bonds in polysaccharides, including cellulose and hemicellulose. The stretching bands of C-O and C-N, observed around 1250 cm⁻¹, exhibited the highest intensity for VW, suggesting the presence of fatty acids. The alcohol groups were detected at about 1150 cm⁻¹, which is due to C=O stretching in the BW and RW samples (Islam et al., 2023). A sharp and intense peak observed at approximately 1050 cm⁻¹ within the spectrum of VW indicates the stretching vibration of C=O bonds, specifically originating from the acetyl groups present in hemicellulose (Ren et al., 2016). The intensive peak observed around 1020 cm⁻¹ is associated with the stretching bands of C-N, indicative of the presence of proteins (Islam et al., 2023; Opatokun et al., 2016). A narrow and sharp peak around 870 cm⁻¹ is related to the C-H stretching band in the MW samples, which implies the presence of lignocellulosic materials. Weak peaks around 710 cm⁻¹ were detected in all waste samples, indicating C=C stretching within aromatic rings.

For the hydrolysates, a similar FTIR spectrum was obtained as untreated RFWs. The FTIR spectra of all hydrolysates showed that peaks at 3400 cm⁻¹, 2920 cm⁻¹, 2850 cm⁻¹, and 1740 cm⁻¹, were significantly reduced or disappeared, indicating that a majority of the carbohydrates (starch, cellulose, and hemicellulose) was hydrolyzed to glucose through MAH. The absorption band around 1640 cm⁻¹ in BWH and RWH samples decreased and disappeared in VWH samples indicating the hydrolysis of proteins. The observed intense peak at 1420 cm⁻¹ for MW decreased greatly after the MAH process indicating the significant hydrolysis of proteins containing S=O bonds.

## Conclusion

FW shows a high potential to produce sugar-rich substrates. However, FW composition is complex and is composed of different foodstuff groups. This study provides valuable insights into the typological (vegetable, fruit, meat, bread, contents, etc), chemical, and elemental analysis of FW from Iranian restaurants to study the relation between FW characteristics and FW treatability through microwave-diluted acid hydrolysis. Under optimal conditions (96.0 °C, 340 W, HCL 1.45 %, and 11.1 min), a high concentration of fermentable sugars was observed for BWH, RWH, VWH, and MWH with 125.18 g/L, 92.11 g/L, 57.24 g/L, and 9.48 g/L, respectively.

## CRediT authorship contribution statement

Payam Torabi: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. Nasser Hamdami: Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. Nafiseh Soltanizadeh: Writing – review & editing, Validation, Supervision. Omidvar Farhadian: Writing – review & editing, Validation, Conceptualization. Alain Le-Bail: Writing – review & editing, Validation, Conceptualization.
<figure><img src="image_10.png" /><figcaption>Fig. 5. FT-IR spectrum of typologically sorted RFW (A) and their hydrolysates (B).</figcaption></figure>

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Data availability**

Data will be made available on request.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.org/10.1016/j.clema.2024.100269.

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