# Discrimination du lait traité thermiquement par spectroscopie de fluorescence combinée à l'ACP et aux réseaux de neurones artificiels

Abderrahmane Aït-Kaddour, Hassan M.A. Abdelbaky, Shaimaa Hamdy, Tahar Boubellouta, Saïd Abou-El-Karam, Hani Sh. Abdelmentolb

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Abderrahmane Aït-Kaddour, Hassan M.A. Abdelbaky, Shaimaa Hamdy, Tahar Boubellouta, Saïd Abou-El-Karam, et al.. Discrimination du lait traité thermiquement par spectroscopie de fluorescence combinée à l'ACP et aux réseaux de neurones artificiels. Journal of Food Composition and Analysis, 2025, 146, pp.107952. 10.1016/j.jfca.2025.107952. hal-05147385

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# Discrimination of thermally treated milk using fluorescence spectroscopy combined with PCA and artificial neural networks

Abderrahmane Aït-Kaddour<sup>a,b,*</sup>, Hassan M.A. Abdelbaky<sup>c</sup>, Shaimaa M. Hamdy<sup>c</sup>, Tahar Boubellouta<sup>d</sup>, Saïd Abou-El-Karam<sup>e</sup>, Hani Sh. Abdelmentolb<sup>c</sup>

## ARTICLE INFO

### Keywords:

Milk

Thermal treatments

Fluorescence

Artificial neural network

## ABSTRACT

This study assessed the use of multilayer perceptron artificial neural networks (MLP-ANN) combined with fluorescence spectroscopy to classify milk samples by species (cow or buffalo), thermal treatment, and cold storage duration. Models were developed using different fluorescence inputs: Vitamin A, tryptophan, synchro- nous spectra (80 nm offset), and a Vitamin A-tryptophan combination. Model performance was evaluated based on classification accuracy, mean square error (MSE), and training efficiency. Principal component analysis (PCA) was also employed to explore the effects of cooling, thermization, and pasteurization on milk's physicochemical properties. PCA results showed clear clustering of samples based on species, thermal treatments, and storage times, with associated differences in pH, lactose, acidity, nitrogen fractions, calcium, and clotting time. MLP- ANN models achieved classification accuracies ranging from 44% to 100% and MSE values from 0% to 14%, depending on input features and classification complexity. Synchronous fluorescence spectra yielded the highest performance, achieving 100% accuracy in distinguishing both species and thermal treatments, high- lighting their sensitivity to heat-induced molecular changes. Simpler classification tasks required fewer training epochs and hidden neurons, while more complex tasks involving multiple variables demonstrated lower per- formance, particularly when using combined tryptophan-Vitamin A inputs.

## 1. Introduction

The most widely used thermal treatments in the dairy industry are chilling, thermization, and pasteurization (McSweeney and Fox, 2003). These processes are essential for inhibiting or destroying undesirable microorganisms, ensuring milk safety, prolonging shelf life, and enhancing dairy product quality (Mazumder et al., 2024). Cooling is adequate for short-term milk storage, while thermization or pasteuri- zation is required for longer storage. Thermization occurs at 57–68 °C for 5–30 s, and pasteurization can be achieved via low-temperature long-time (LTLT) at 63–66 °C for 30–32 min or high-temperature short-time (HTST) at 72 °C for 15 s. Both thermized and pasteurized milk are stored at 4–6 °C, with HTST-pasteurized milk remaining fresh for 10–14 days at 9 °C (Pegu and Arya, 2023).

These thermal treatments induce several physicochemical and structural changes in milk, impacting its nutritional and technological properties. Notable changes include the irreversible denaturation and aggregation of whey proteins, their association with casein micelles, the dissociation of κ-casein (van Lieshout et al., 2019), and the insolubili- zation of colloidal calcium phosphate into casein particles. Meanwhile, cooling milk to 5 °C dissolves a small amount of colloidal salts, which can influence its coagulation properties by weakening the gel network and affecting the quality of dairy products like cheese (de la Fuente et al., 1997; Donato and Guyomarc'h, 2009; Fox et al., 2015).

Most official methods to assess the effects of thermal treatments in milk focus on evaluating physicochemical changes in specific milk components (McSweeney and Fox, 2003). However, there is a growing need for sensitive, cost-effective, and rapid analytical techniques to

<footnote><sup>a</sup> Université Clermont Auvergne, VetAgro Sup, INRAE, UMRF, Avenue de L'Europe, Lempdes 63370, France</footnote>

<footnote><sup>b</sup> Laboratory of Food Chemistry, Department of Food Technology, Universitas Padjadjaran, Bandung, Indonesia</footnote>

<footnote><sup>c</sup> Dairy Department, Faculty of Agriculture, Fayoum University, Fayoum 63511, Egypt</footnote>

<footnote><sup>d</sup> Laboratory of Characterization and Valorization of Natural Resources, Faculty of Natural and Life Sciences, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, Bordj Bou Arreridj 34000, Algeria</footnote>

<footnote><sup>e</sup> INRAE, UR QuaPA, Saint-Genés-Champanelle F-63122, France</footnote>

<footnote>* Corresponding author at: Université Clermont Auvergne, VetAgro Sup, INRAE, UMRF, Avenue de L'Europe, Lempdes 63370, France.
E-mail address: abderrahmane.aitkaddour@vetagro-sup.fr (A. Aït-Kaddour).</footnote>
identify the type of thermal treatment milk has undergone before being processed into dairy foods (e.g., cheese). This is particularly important for respecting production specifications, facilitating trade, and detecting fraud. For example, the French PDO (Protected Designation of Origin) Salers cheese must be produced exclusively from raw cow milk (Didienne et al., 2012), whereas Cantal PDO cheese can be made from either raw or pasteurized cow milk (Chatelard-Chauvin et al., 2015).

Fluorescence spectroscopy, when combined with chemometric tools, has emerged as a promising technique for assessing structural changes in milk components (Radotić et al., 2023; Freire et al., 2024). This technique is fast and sensitive, requiring minimal sample preparation and no need for highly skilled operators (Kulmyrzaev et al., 2005; Shaikh and O'Donnell, 2017). It measures emission and/or excitation spectra of naturally occurring fluorescent compounds in milk, such as tryptophan, vitamin A, and riboflavin. For example, Ullah et al. (2020) demonstrated that fluorescence induced by tryptophan and vitamin A could reliably differentiate milk samples based on their types. Similarly, Kamal and Karoui (2017) used fluorescence spectroscopy to monitor molecular changes in camel milk subjected to various thermal treatments, identifying clear distinctions between samples heated above and below 70 °C. These studies emphasize the potential of fluorescence spectroscopy and chemometric methods to differentiate heating treatments through subtle structural changes in proteins and lipids, which are highly sensitive to their molecular environment (Moro et al., 2001). Principal component analysis (PCA) and partial least squares regression (PLSR) are commonly used linear techniques for analyzing such spectral data (Ma and Amamcharla, 2021; Trygg et al., 2007).

In addition, non-linear approaches based on artificial intelligence, such as artificial neural networks (ANNs), are increasingly applied to spectral data analysis. ANNs, which improve through experience via a feedforward strategy, include algorithms like the multi-layer perceptron (MLP-ANN), where neurons are arranged in one or more hidden layers and trained using backward propagation (Siripatrawan and Harte, 2007; Chan et al., 2023). This method has been applied for different purposes in dairy research. For example, ANN was recently used in combination with middle infrared spectroscopy to predict the detailed milk mineral profile in dairy cattle (Bisutti et al., 2024). Vásquez et al. (2018) also used ANN for modeling the hardness of Swiss-type cheese during ripening. Similarly, it was applied to predict ripening time in cheeses made from mixtures of cow's, ewe's, and goat's milk using gas chromatography (GC) and near-infrared spectra (Soto-Barajas et al., 2013). In the same context, ANN was used to classify Pecorino cheese based on ripening time and manufacturing techniques, employing data from electronic noses and GC-MS analysis of volatile substances (Cevoli et al., 2011). Additionally, ANNs have been used to predict seasonal variations in butter production by analyzing fatty acid composition (Gori et al., 2011), and to assess animal feeding regimens using NIR spectroscopy of milk (Gori et al., 2011). Gas chromatography paired with ANNs has also been employed to determine the geographical origin of cheeses—for example, tracing typical cheeses from Piemonte, Italy via mono- and sesquiterpenes (Giuseppe et al., 2005), and discriminating the origin of Emmental cheese (Pillonel et al., 2005). However, to date, no study has explored the use of ANN combined with fluorescence spectroscopy to differentiate milk based on thermal treatments.

Therefore, this study aims to present a novel approach by being the first to combine fluorescence spectroscopy with a multilayer MLP-ANN for the classification of milk subjected to different thermal treatments. Specifically, it aims to explore the effectiveness of this integrated method in differentiating milk samples that have undergone cooling, thermization, and pasteurization, followed by subsequent cold storage. Furthermore, PCA was used to evaluate the impact of these thermal treatments and storage conditions on the physicochemical properties of milk, offering a comprehensive understanding of the structural and compositional changes involved.

# 2. Materials and methods

## 2.1. Milk samples

Buffalo (Egyptian herd) and cow (Red-and White Holstein herd) milk samples were collected in two-liter glass containers immediately after morning milking from a private dairy farm in Fayoum, Egypt. Upon collection, the two-liter glass packages containing the freshly obtained milk samples were immediately transferred to the laboratory facilities under controlled conditions by using a constant temperature box to prevent any temperature fluctuations or exposure to external factors that could alter the milk properties.

## 2.2. Milk thermal treatments

To assess the effects of cooling, thermization and pasteurization on the milk samples, 2 liters of raw milk from each animal (cow and buf- falo) were processed using the same procedure. In brief, the initial 2 liters were divided into four equal 500 ml portions. One portion was left untreated as the control sample, referred to as raw milk, for comparison. The other three portions underwent thermal treatments, including cooling, thermization, and pasteurization.

Cooling treatment was performed in a laboratory refrigerator (LG brand, Korea) by keeping the sample at 5 ± 1 °C for 2 h. This cooling process aimed to assess the immediate effects of temperature reduction on the composition of milk and replicate refrigerated conditions that are frequently employed for short-term milk storage. For thermization, the milk sample was performed at 63°C for 15 s (Deeth and Lewis, 2017) by using an APV® heat plate pasteurizer Model HXP (United Kingdom). Pasteurization was performed at 72°C in 15 s by using the same APV® heat plate. The thermized and pasteurized milk samples were promptly transferred to a refrigerated storage unit set at 5°C to maintain consis- tent post-treatment conditions. The samples were stored at 0, 24, 48, and 72 h to evaluate the impact of thermal treatments on milk stability and composition over time.

## 2.3. Physicochemical analysis

Titratable acidity was determined using a titration method with 0.1 N NaOH and phenolphthalein indicator as described in AOAC (2012) method 981.12. The pH values were measured at 25°C as described in AOAC (2012) method 981.12 using a digital pH meter (model Kent EIL 7020, UK). Fat content was determined using Gerber's method accord- ing to AOAC (2012) method 922.06, and protein content was analyzed using the Kjeldahl nitrogen method with a protein conversion factor of 6.38 according to AOCA (2012) method 954.01. Total solids were measured by drying 10 ml of sample at 105°C for 3.5 h as described in AOAC (2012) method 925.40. Ash content was determined using an ashing method at 550°C in a muffle furnace as described in AOAC (2012) method 942.05. Lactose content was calculated by subtracting the sum of moisture, protein, fat, and ash percentages from 100 %. Total calcium and soluble calcium were measured according to IDF (International Dairy Federation) methods (IDF, 1992), with colloidal calcium calcu- lated as the difference between total and soluble calcium. Protein breakdown was assessed by measuring water-soluble nitrogen at pH 4.6 (% WSN) and 12 % TCA-soluble nitrogen (% NPN, non-protein nitrogen) using the Kjeldahl method. All experiments were performed in triplicate to ensure the reproducibility and reliability of the results.

## 2.4. Rennet coagulation time and curd syneresis

Rennet coagulation time (RCT) was carried out following the method described by Domagala et al. (2016). The milk samples (10 ml) were adjusted to the renneting temperature (38°C), 20 µl of calcium chloride, and 200 µl of chymosin (Chr Hansen, Denmark) was added to milk with stirring. A stopwatch was used to calculate RCT in seconds from rennet
addition until the beginning of milk coagulation.

Curd syneresis (%) was measured as described by Arango et al. (2013). Briefly, the syneresis (grams of whey per kilogram of curd milk) was calculated as the weight of whey in grams released from each kilogram of curd milk in the package after the different storage times divided by the weight of milk of the same package in grams and multiplied by 100.

## 2.5. Intrinsic fluorescence spectroscopy

Molecular structure changes in milk components were monitored at a controlled temperature of 20°C using a FP-7500 fluorescence spectrophotometer (JASCO Technologies, MD, USA). The spectrophotometer used a 15 W Xenon lamp for excitation and a front-face geometry accessory at an angle of incidence of 56° to measure fluorescence emissions. Emission spectra of tryptophan residues, a fluorescent component in milk proteins, were recorded between 300 and 400 nm after excitation at 290 nm. Vitamin A fluorescence was monitored by exciting the samples between 250–350 nm with emission fixed at 410 nm. One synchronous fluorescence spectrum was collected by simultaneously scanning excitation and emission monochromators in the 250–550 nm range with a constant wavelength difference ($\Delta\lambda$ = 80 nm) (Boubellouta and Dufour, 2010).

Fluorescence data were collected for each milk sample submitted to different conditions (raw, cooled, thermized, pasteurized) and storage times (0, 24, 48, and 72 h) for both cow and buffalo milk. A total of 104 spectra were obtained. The data were organized into four distinct batches for analysis: batch A by animal species (cow and buffalo), batch B by thermal treatments (raw, cooled, thermized and pasteurized), batch C by storage time (24, 48, and 72 h), and batch D by thermal treatments for each species (Fig. 1). To assess the discriminatory power of the fluorescence data, MLP-ANN was applied to each batch.

## 2.6. Chemometrics

Two chemometric methods were applied in this work, PCA and MLP-ANN. PCA was used only as an exploratory data analysis to give a general overview of the sample separation and for variable reduction for

physicochemical and spectral data. On the other hand, MLP-ANN analysis was applied to spectral data for a quantitative discrimination point of view.

### 2.6.1. Principal component analysis

The PCA was applied to the physicochemical data and, in a second step, to the spectral data in order to highlight the separation of the samples, particularly by the score plots. PCA is utilized to lower the dimensionality of spectral data, enabling a clearer understanding of the potential for object differentiation and generating Principal Component (PC) scores (Kurita, 2020). This technique is applied without considering any correlation between dependent and independent variables (de Oliviera et al., 2025). The cumulative variance parameter rate (>90 %) criteria was used for selecting the number of PC retained after PCA dimensionality reduction for both physicochemical and spectral data.

In order to highlight differences between spectra, Analysis of Variance (ANOVA) was applied to each wavelength to identify the relevant and significant wavelength ranges ($p$ < 0.05) that play a key role in discriminating between samples. The analysis was performed on raw spectra using the Matlab® software (R2012b 8.0.0.783, The Mathworks Inc., Natic, MA, USA) and the PLS-toolbox 852 (Eigenvector Research, Inc., Manson, WA, USA), ensuring a robust and systematic approach to data processing and interpretation.

### 2.6.2. MLP-artificial neural network

The MLP-ANN analysis focused on quantitatively discriminating spectral data to classify samples based on their respective classes. Feed-forward pattern recognition networks proposed in prttool (Neural Network Pattern Recognition Tool) for Matlab® (patternnet function) was used to classify spectral data (i.e. inputs) based on outputs targeted batch classes (batches 1–4) (Fig. 1). The neural network was trained by using the Levenberg-Marquardt algorithm (trainlm function). The trainlm function employs a backpropagation algorithm that calculates the deviations between the actual and predicted values until it reaches a minimum when the iteration with differing epoch updates the weight of different vectors in MLP-ANN. Epoch refers to the number of passes a training dataset makes around an algorithm. When the number of epochs is too low, the neural network will not be sufficiently trained,

<figure><img src="image_3.png" /><figcaption>Fig. 1. Experimental design of the study.</figcaption></figure>
creating underfitting. If, on the other hand, the number of epochs is too high, the neural network risks overfitting. It will then be unable to make predictions with real data.

Prior to conducting network calculation, the different spectra (tryptophan, Vitamin A, and synchronous spectra) were pre-processed to improve data quality and consistency using SNV (standard normal variate), MSC (multiple scattering correction) or AREA (spectra normalization by reducing their area under the curve to a value of 1) method. The combination of spectral data sets to assess their reliability in enhancing the performance of the MLP-ANN models was investigated. This approach likely aimed to determine whether integrating multiple spectral data sets could provide additional relevant features or improve the model's ability to distinguish between different classes or conditions. Combinations of data sets were performed by a simple concatenation (i.e. low-level data fusion) of the different spectral data matrix, giving at the end a total of four data matrices to analyze (1-Tryptophan; 2-Vitamin A; 3-Synchronous spectra; 4-Tryptophan + Vitamin A). The data sets were divided into a training sample set (containing 70 % of samples: n = 72), and validation and test sample sets containing each one 15 % of the samples (n = 16 × 2). The division was obtained by using the *dividend function* (i.e. data randomly divided) in Matlab®. The neural network architecture consisted of four layers (one input, two hidden, and one output) with an iterative process for optimizing the network structure to minimize mean square error (MSE) and maximize global accuracy. In order to find the best model pattern within this restricted range (number of hidden neurons layers, data matrix, and preprocessing), the pattern was started with the simplest architecture of one hidden layer and one node, and increased until the network produced the lowest error of prediction (mean square error, MSE) (Eq. 1) (Pandey et al. 2022), the number of epoch (training length), and the highest global accuracy. The global accuracy was calculated based on the confusion matrix by using the four factors, true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) (Eq. 2) (Aït-Kaddour et al. 2024), to comprehensively assess the model's

performance.

$$MSE = \frac{1}{N} \sum_{i=1}^{N} (y - \hat{y})^2 \quad (1)$$

$$Accuracy = \frac{(TP + TN)}{(TP + TN + FP + FN)} \times 100 \quad (2)$$

## 2.7. Statistical analysis

All analyses were performed in triplicate. The physicochemical data were presented in the tables as mean values ± standard deviation and analyzed statistically using two-way analysis of variance (ANOVA) followed by LSD (P ≤ 0.05) for the comparison among means using XLSTAT statistical software version 2007 (Addinsoft Inc, Paris, France).

# 3. Results and discussion

## 3.1. Analysis of physicochemical data

Table 1 shows the results of the physicochemical analysis of both buffalos' and cow's milk as influenced by thermal treatments. It was observed that physicochemical and biochemical characteristics between milk samples, depending on the thermal treatment, were significantly different between raw, cooled, thermized and pasteurized milk.

Across both buffalo and cow milk, the pH, acidity, fat content, soluble nitrogen, non-protein nitrogen, colloidal calcium, and total calcium content remained relatively stable across all treatments. Minimal fluctuations were observed in these parameters in both milk types, indicating that they were not significantly affected by heat treatments.

Both buffalo and cow milk exhibited a decrease in soluble calcium content following heat treatments. In buffalo milk, soluble calcium decreased from 0.05 mg/100 g in raw milk to 0.03 mg/100 g after thermization and pasteurization, while in cow milk, it decreased from

<table><caption>**Table 1**
Physicochemical analysis of cow's and buffalo's milk as function of thermal pretreatments.</caption><thead><tr><th></th><th>Raw milk</th><th>Cooling (5°C)</th><th>Thermization</th><th>Pasteurization</th><th>Significance</th></tr></thead><tbody><tr><td><strong>Buffalo milk</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>pH</td><td>6.60 ± 0.10a</td><td>6.57 ± 0.06a</td><td>6.60 ± 0.0a</td><td>6.57 ± 0.06a</td><td>NS</td></tr><tr><td>Acidity (%)</td><td>0.16 ± 0.01a</td><td>0.16 ± 0.01a</td><td>0.16 ± 0.01a</td><td>0.16 ± 0.01a</td><td>NS</td></tr><tr><td>Total Solids (%)</td><td>14.24 ± 0.02a</td><td>14.42 ± 0.03a</td><td>14.85 ± 0.03b</td><td>15.00 ± 0.05b</td><td>**</td></tr><tr><td>Fat (%)</td><td>5.90 ± 0.00a</td><td>5.87 ± 0.06a</td><td>6.00 ± 0.10a</td><td>6.00 ± 0.10a</td><td>NS</td></tr><tr><td>Protein (%)</td><td>3.14 ± 0.19a</td><td>3.16 ± 0.02a</td><td>3.21 ± 0.03a</td><td>3.28 ± 0.02a</td><td>NS</td></tr><tr><td>Ash (%)</td><td>0.74 ± 0.02a</td><td>0.73 ± 0.04a</td><td>0.77 ± 0.02</td><td>0.78 ± 0.03a</td><td>NS</td></tr><tr><td>Lactose (%)</td><td>4.64 ± 0.12a</td><td>4.66 ± 0.06a</td><td>4.87 ± 0.06b</td><td>4.94 ± 0.05b</td><td>**</td></tr><tr><td>Soluble Nitrogen (%)</td><td>0.06 ± 0.01a</td><td>0.06 ± 0.01a</td><td>0.06 ± 0.01a</td><td>0.06 ± 0.01a</td><td>NS</td></tr><tr><td>Non-protein nitrogen (%)</td><td>0.03 ± 0.01a</td><td>0.03 ± 0.01a</td><td>0.04 ± 0.01a</td><td>0.04 ± 0.01a</td><td>NS</td></tr><tr><td>Soluble Ca (mg/100 g)</td><td>0.05 ± 0.01a</td><td>0.06 ± 0.01a</td><td>0.03 ± 0.01b</td><td>0.03 ± 0.01b</td><td>*</td></tr><tr><td>Colloidal Ca</td><td>0.11 ± 0.01a</td><td>0.11 ± 0.01a</td><td>0.15 ± 0.01b</td><td>0.17 ± 0.02b</td><td>**</td></tr><tr><td>Total Ca (mg/100 g)</td><td>0.16 ± 0.01a</td><td>0.17 ± 0.01a</td><td>0.18 ± 0.00b</td><td>0.20 ± 0.01b</td><td>**</td></tr><tr><td>Rennet clotting time (sec)</td><td>50.96 ± 2.77a</td><td>50.28 ± 0.08a</td><td>51.17 ± 0.17b</td><td>51.83 ± 0.04b</td><td>NS</td></tr><tr><td>Syneresis (%)</td><td>47.19 ± 1.77a</td><td>51.15 ± 1.28b</td><td>52.21 ± 0.22c</td><td>52.55 ± 0.29c</td><td>**</td></tr><tr><td><strong>Cow milk</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>pH</td><td>6.60 ± 0.00a</td><td>6.63 ± 0.06a</td><td>6.57 ± 0.06a</td><td>6.57 ± 0.06a</td><td>NS</td></tr><tr><td>Acidity (%)</td><td>0.16 ± 0.00a</td><td>0.16 ± 0.00a</td><td>0.17 ± 0.01a</td><td>0.16 ± 0.00a</td><td>NS</td></tr><tr><td>Total solids (%)</td><td>12.85 ± 0.12a</td><td>12.86 ± 0.04a</td><td>12.88 ± 0.1b</td><td>12.97 ± 0.02b</td><td>*</td></tr><tr><td>Fat (%)</td><td>4.50 ± 0.00a</td><td>4.50 ± 0.00a</td><td>4.57 ± 0.06a</td><td>4.60 ± 0.00a</td><td>NS</td></tr><tr><td>Protein (%)</td><td>3.13 ± 0.25a</td><td>3.13 ± 0.10a</td><td>3.10 ± 0.02a</td><td>3.11 ± 0.03a</td><td>NS</td></tr><tr><td>Ash (%)</td><td>0.70 ± 0.02a</td><td>0.70 ± 0.01a</td><td>0.71 ± 0.02a</td><td>0.72 ± 0.01a</td><td>NS</td></tr><tr><td>Lactose (%)</td><td>4.53 ± 0.01a</td><td>4.53 ± 0.01a</td><td>4.51 ± 0.01a</td><td>4.52 ± 0.01a</td><td>NS</td></tr><tr><td>Soluble nitrogen (%)</td><td>0.08 ± 0.01a</td><td>0.08 ± 0.01a</td><td>0.08 ± 0.01a</td><td>0.08 ± 0.02a</td><td>NS</td></tr><tr><td>Non-protein nitrogen (%)</td><td>0.03 ± 0.01a</td><td>0.03 ± 0.01a</td><td>0.03 ± 0.01a</td><td>0.03 ± 0.01a</td><td>NS</td></tr><tr><td>Soluble Ca (mg/100 g)</td><td>0.03 ± 0.01a</td><td>0.03 ± 0.01a</td><td>0.02 ± 0.01b</td><td>0.02 ± 0.01b</td><td>*</td></tr><tr><td>Colloidal Ca</td><td>0.05 ± 0.01a</td><td>0.06 ± 0.01a</td><td>0.06 ± 0.01b</td><td>0.06 ± 0.01b</td><td>*</td></tr><tr><td>Total Ca (mg/100 g)</td><td>0.08 ± 0.01a</td><td>0.09 ± 0.01a</td><td>0.08 ± 0.01b</td><td>0.08 ± 0.01b</td><td>*</td></tr><tr><td>Rennet clotting time (sec)</td><td>50.67 ± 4.04a</td><td>50.33 ± 1.53a</td><td>57.67 ± 2.52a</td><td>59.67 ± 2.52a</td><td>NS</td></tr><tr><td>Syneresis (%)</td><td>41.23 ± 0.01a</td><td>41.41 ± 0.08b</td><td>43.75 ± 0.26c</td><td>44.56 ± 0.2c</td><td>**</td></tr></tbody></table>

Means within the same row with different superscripts (a–c) differ significantly (P < 0.05), NS: Not significant.

\*: Significant at P = 0.05–0.01; \*: Significant at P < 0.01.
0.03 mg/100 g to 0.02 mg/100 g, showing a similar trend across both milk types.

Several parameters increased in both milk types as a result of heat treatments. Total solids, protein, ash, colloidal calcium, and total calcium content increased in both buffalo and cow milk. Buffalo milk showed more pronounced increases, particularly in total solids and total calcium content. Syneresis also increased in both milk types, with buffalo milk showing a greater increase, from 47.19 % in raw milk to 52.55 % after pasteurization, compared to the increase from 41.23 % to 44.56 % in cow milk. Rennet clotting time increased in both milk types, with cow milk showing a more significant increase, from 50.67 s in raw milk to 59.67 s after pasteurization, compared to the slight increase in buffalo milk from 50.28 s to 51.83 s.

The physicochemical data of the two milk types were further analyzed by PCA to better identify differences between samples and their potential clustering. The PCA scores and loading plots provide valuable insights into the variance explained by the principal components, and the contribution of individual variables to sample clustering as shown in Fig. 2 (A-F). Notably, the first three principal components (PCs) collectively accounted for over 93 % of the variance (PC1: 61.80 %; PC2: 24.12 %; PC3: 7.21 % of the total variance).

When looking to effect of thermal treatments (Fig. 2 A, B), the separation between the raw and cooled milk groups and the groups of milk submitted to heating treatment (thermization and pasteurization) is mainly associated to soluble calcium and rennet clotting time. The position of samples in the positive quadrant of PC3 showed that raw and cooled milk exhibited the highest soluble $Ca^{2+}$ and soluble nitrogen contents compared to the other treatments. This finding aligns with the results from Table 1, where both buffalo and cow milk showed a decrease in soluble calcium content following thermal treatments. This confirms the negative correlation between soluble calcium concentration and increasing temperature, as noted in previous research (Wang and Ma, 2020). On the other hand, the negative quadrant of PC3 showed that thermized milk and pasteurized milk presented higher values for rennet clotting time and syneresis. These trends are consistent with the data presented in Table 1, where both buffalo and cow milk showed an increase in rennet clotting time following heat treatments, with cow milk showing a more significant increase. Similar observations regarding the effects of heat treatment on these properties have been reported by other authors. For example, Ustunol and Brown (1985) found that milk's clotting time, curd firming rate, and curd cutting time all significantly decreased at 75°C. Furthermore, Wang and Ma (2020) noted that increasing temperature from 25 to 50°C and prolonged heating reduced the pH and clotting time of milk.

The observation of the Fig. 2C, demonstrated that the separation of the cow milk and buffalo milk samples can be observed on the PC₁ axis. The cow milk samples presented negative values on PC₁, while the buffalo milk samples presented positive values on the same axis. The observation of the Fig. 2C suggested that higher acidity and soluble nitrogen are exhibited by the cow milk samples, while buffalo milk presented higher values for all the other physicochemical and biochemical characteristics and especially for total solids, $Ca^{2+}$ and syneresis. The renneting time seems to be equivalent for both types' milks. Those results align with previous studies conducted by Ahmad et al. (2008), which also reported that buffalo milk is richer in fat, lactose, protein (especially caseins), and minerals such as calcium, magnesium and inorganic phosphate compared to cow milk.

The PCA representation based on storage time (Fig. 2 E, F) showed that samples groups are discriminated along the PC₂ axis based on their storage time (Fig. 2F). It can be observed that the score values increase when storage time increase. Moreover, it can be observed that samples stored for 0 h and 24 h were separated from samples stored for 48 h and 72 h. The observation of the variables map (Fig. 2E) highlighted that samples stored at 0 h /24 h presented higher pH and lactose content, while lower values were observed for acidity, non-protein nitrogen, soluble nitrogen, rennet clotting time, and soluble calcium. These results

align with those of O'Connell et al. (2017), who observed an increase in the concentrations of certain free amino acids (glutamic acid, lysine, and arginine) during cold storage (96 h at 2°C and 6°C), indicating proteolytic activity. However, minimal deterioration was observed for rennet coagulation time, curd firmness, and heat stability.

## 3.2. Visual inspection of fluorescence spectra

### 3.2.1. Effect of animal species on milk fluorescence spectra

Tryptophan emission spectra, Vitamin A excitation, and synchronous fluorescence spectra of raw cow and buffalo milk are presented in Fig. 3A. The tryptophan emission fluorescence averaged spectra of cow's and buffalo's milk exhibited clear differences in shape and intensity. Specifically, when comparing the spectrum of cow milk to buffalo milk, it was evident that the cow milk spectrum presented higher overall fluorescence intensity. One possible reason for this difference in intensity might come from variations in the protein concentration of tryptophan residues in proteins, which are intrinsic fluorescent probes found in proteins. Moreover, the buffalo milk spectra presented a specific shape with two peaks exhibiting a maximum intensity at 346 and 366 nm (Fig. 3, A1). The presence of these peaks at specific wavelengths suggests that buffalo milk may have different molecular components or structural arrangements (e.g. molecules fold, bond, or interact with one another) for lipids and proteins compared to cow milk.

Concerning excitation spectra of Vitamin A (Fig. 3, A2) in cow and buffalo milk samples, both milk spectra exhibited three prominent peaks at 288, 309, and 324 nm. However, differences observed in fluorescence intensities can be reported for the two peaks maximum intensity located at 288 and 309 nm between cow and buffalo milk, contrary to the peaks located at 324 nm, which showed consistent intensities across the two milk species. Compared to the cow milk spectrum, the buffalo milk spectrum presented lower intensity for the peak centered at 288 nm, while the opposite was observed for the peak centered at 309 nm. The synchronous fluorescence spectra (Fig. 3, A3) exhibited a more complex pattern, with five bands, three small ones exhibiting a maximum at 317, 402 and 439 nm, and two prominent ones with a maximum at 358 and 534 nm. The assignment of these bands was previously discussed by Boubellouta and Dufour (2008). It is generally reported that synchronous spectra of milk bands observed between 300 and 600 nm are related to components such as Vitamin A, tryptophan residues in protein, riboflavin, and carotenoid components (Ullah et al., 2020). More precisely, the shoulder located at 317 nm has been associated with Vitamin B₆ (Pyridoxine) (Xue et al., 2016; Yang et al., 2016). The 358 nm band was previously assigned to the fluorescence of tryptophan residues, and the band located at 402 nm was ascribed to Vitamin A. Maillard-reaction products in milk may be related to the band at 439 nm (Birlouez-Aragon et al., 1998; Feinberg et al., 2006). The broadband exhibiting a maximum at 534 nm was previously assigned to riboflavin (Vitamin B₂) in milk (Boubellouta and Dufour, 2008; Karoui et al., 2005). The most significant difference in band intensities and peak maximum wavelength position between the two-milk species can be identified between 300 and 478 nm.

Tryptophan, an important intrinsic fluorescent probe, is an amino acid used in protein biosynthesis. The difference in tryptophan spectra can be associated to difference in protein structure and concentration between cow and buffalo milk. Studies have shown that cow milk contains a higher concentration of tryptophan residues compared to buffalo milk (Shapouri et al., 2001). Buffalo milk exhibit also has higher concentrations of casein subfractions (αS1-, β-, and κ-) compared to bovine milk. Approximately 99 % of the casein in buffalo milk is in micellar form, while in bovine milk, 90–95 % is micellar, with the remainder in the soluble phase. It was also reported that buffalo and bovine milk casein micelles are smooth and circular, but buffalo milk micelles are larger (70–200 nm) compared to bovine milk micelles (50–180 nm). Additionally, buffalo milk casein micelles are less hydrated and contain more colloidal calcium and inorganic phosphate than those in bovine
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Principal component analysis loadings (A, B, E) and associated scores (B, D, F) plots of physicochemical data of milk samples. BF (▼): Buffalo, CO (*): Cow; storage time: 0 h (▼), 24 h (*), 48 h (■) and 72 h (+); thermal treatments: raw (■), cooling (▼), thermization (+) and pasteurization (*).</figcaption></figure>

milk (Mejares et al., 2022). Fluorescence properties of this amino acid are very sensitive to the surrounding protein environment (Hellmann and Scheider, 2019) that can explain the difference observed between

those two milk spectra. Vitamin A, a fat-soluble molecule with conjugated double bond, is a good fluorophore. Differences observed in Vitamin A fluorescence spectra could be due to difference in physical
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** (A) Mean fluorescence spectra of tryptophan (Trp), Vitamin A (Vit A), and synchronous (Sync) (Δλ = 80 nm) fluorescence spectra of raw cow (blue line) and buffalo (red line) milk. (B) Plots of ANOVA results: Fisher F value (blue curve) and its associated *p*-value (red curve) indicating the relevant wavelength ranges for sample discrimination. (C) PCA scatter plots of PC₁, PC₂, and PC₃ showing sample separation according to animal factor. (A1: Tryptophan, A2: Vitamin A, A3: Synchronous).</figcaption></figure>

state of triglycerides between the two milk species (Karoui et al., 2004; Mohammed Loudiyi et al., 2018), and differences in the protein-lipid interaction. However, the differences between those landscapes can be related to differences in milk composition (Fig. 2a). The differences in maximum intensities and band positions observed in tryptophan, Vitamin A, and synchronous fluorescence spectra suggested specific molecular spectral landscape for buffalo and cow milk, respectively. This specificity has been used in the studies by Ullah et al. (2020), Genis et al. (2020) and Elgarhi et al. (2020) to discriminate different milk species, such as cow and buffalo milk. Therefore, PCA was applied to spectra in order to identify the potential of fluorescence spectra to discriminate between the two-milk species. It was found that the fluorescence spectra, whether based on tryptophan, Vitamin A, and synchronous fluorescence, effectively discriminate between the two types of milk. This approach showcases the potential of fluorescence spectroscopy as a valuable tool for characterizing and differentiating between different types of milk based on their unique spectral signatures.

### 3.2.2. Effect of thermal treatment on milk fluorescence spectra

In previous studies (Ayala et al., 2020; Kamal and Karoui, 2017;

Kulmyrzaev et al., 2005), raw milk was defined as milk that had been chilled to 4–5°C. However, in the present study, raw milk refers to milk that has not been chilled after milking. This difference is important because it provides the basis for understanding the thermal effects on milk fluorescence spectra. Fig. 4 presents the changes in fluorescence intensities of tryptophan, Vitamin A, and synchronous fluorescence spectra of each milk species (i.e. buffalo and cow) in response to different thermal treatments (i.e. chilled, thermized and pasteurized). The fluorescence intensities of tryptophan, Vitamin A and synchronous fluorescence spectra in both milk species were found to be affected by thermal treatments. These changes can be considered as “thermal markers” in milk (Ali et al., 2019). These observations support previous studies (Andersen and Mortensen, 2008; Christensen et al., 2006) that have also noted changes in these fluorescence spectra (increase, decrease and/or shift) in milk when submitted to heating.

When considering tryptophan spectra, the highest intensity was observed for cow's milk, then milk undergoing thermization, followed by pasteurized milk, and finally raw milk. In contrast, for buffalo milk, the highest intensity was noted for raw milk. This difference in behavior between cow and buffalo milk suggests species-specific responses to
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Effect of thermal treatments on (A) tryptophan (Trp), Vitamin A (Vita), and synchronous (Sync) fluorescence spectra of cow's (1, 2 and 3) and buffalo's (4, 5 and 6) milk and (B) their respective ANOVA results: Fisher F value (blue curve) and associated p-value (red curve) after spectral data comparison. Thermal treatments: raw (R), cooling (C), thermization (T), and pasteurization (P).</figcaption></figure>

heating treatments. Increasing the severity of heating treatment induced a decrease in the tryptophan fluorescence intensity from chilled milk to pasteurized milk for both cow and buffalo milk. This trend aligns with previously observed observation noted in the study of Dufour and Riaublanc (1997) and Kulmyrzaev et al. (2005). However, other studies have reported that the fluorescence intensity of tryptophan increased with increasing temperature (Pásztor-Huszár and Farkas, 2008). The decrease in fluorescence intensity of tryptophan was associated with the

denaturation of protein that resulted in lesser intensity of tryptophan fluorescence, while the increase was associated with the unfolding of whey proteins (β-lactoglobulin and α-lactalbumin) and exposure of tryptophan residues (Akkerman, 2012). This phenomenon is well documented in the literature. β-lactoglobulin (β-LG) and α-lactalbumin, representing approximately 50 % and 20 % of whey proteins, respectively (Farrell et al., 2004) can be denatured (lose of their native globular structure) when exposed to temperatures above 60°C
<figure><img src="image_7.png" /><figcaption>Fig. 4. (continued).</figcaption></figure>

(Dannenbergand Kessler, 1988; Kontopidis et al., 2004; Creamer et al., 2011). This structural disruption exposes reactive sites, leading to protein aggregation. Moreover, denatured whey proteins can associate with each other or with other proteins, forming complexes (Carbonaro et al., 1998; Tolkach and Kulozik, 2007). Among the most common heat-induced aggregates are β-LG-κ-casein complexes, which attach to the surface of casein micelles through disulfide bonds and hydrophobic interactions (Corredig and Dalgleish, 1996; O'Connell and Fox, 2011).

This difference in spectral variations between cow and buffalo milk may be related to the severity of the heating treatments and the processing methods used (i.e. simple water bath and dairy equipment), as well as the original pH and composition of milk (Shapouri et al., 2001). Nonetheless, based on the F-value and *p*-value (Fig. 4b) no significant differences can be observed between the fluorescence spectra based on

the milk thermal treatment.

The Vitamin A spectra presented three bands centered at 288, 309 and 324 nm. Differences in fluorescence intensity were observed for the three bands depending on thermal treatment. For cow milk, bands at 288 and 309 nm presented almost the same trend with the highest intensity observed in chilled milk, followed by the thermized milk, then pasteurized milk and raw milk showing the lowest intensity. For the 324 nm band, chilled, thermized and pasteurized milks presented equivalent intensities, while raw milk exhibited the lowest intensity. The buffalo milk spectra exhibited a different trend compared to cow milk spectra. For the 288 nm band, a decrease was noted depending on the severity of the heating treatment (raw, chilled, and thermized/pasteurized). The other two bands presented an equivalent trend, with the highest intensity observed in chilled milk, followed by the thermized
milk, then the pasteurized milk and finally raw milk. Based on the F-value and p-value, no significant differences can be reported between spectra depending on the thermal treatment undergone by cow and buffalo milk (Fig. 4). Nonetheless, heat treatment can modify fat globules and proteins and therefore the environment of Vitamin A and consequently its fluorescence properties (Boubellouta et al., 2011). The Vitamin A molecule is located inside the milk fat globule and its excitation and emission depend upon the physical state of triglycerides in fat globules (Loudiyi et al., 2018). Brandao et al. (2019) reported that

heating treatment of milk has a complex effect on Vitamin A fluorescence. They observed a rapid decrease in Vitamin A lifetimes between 25 °C and nearly 40 °C and a slow increase in Vitamin A lifetimes between 40 °C and 85 °C.

Concerning the synchronous fluorescence spectra of buffalo and cow milk, the tryptophan and Vitamin A peaks exhibited the same trends as previously described for classical front face fluorescence spectra of Vitamin A and tryptophan. No significant differences were observed in the Vitamin A and tryptophan wavelength regions, as shown in the F-

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Effect of storage time at 5 °C for 0, 24, 48 and 72 h on (A) tryptophan, Vitamin A, and synchronous fluorescence spectra of cow's (1, 2 and 3) and buffalo's (4, 5 and 6) milk and (B) their respective ANOVA results: Fisher F value (blue curve) and associated p-value (red curve) after spectral data comparison. Vitamin A (Vita), tryptophan (Trp), Synchronous spectra (Sync).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 5. (continued).</figcaption></figure>

value and P-value charts (Fig. 4 B). For the riboflavin region (with maximum intensity at 484 nm) of cow and buffalo milks, an increase in fluorescence intensity was observed in the following sequence: chilled, raw, thermized, and pasteurized. This increase was previously observed after thermal treatment and associated with milk product oxidation. Indeed, this fluorophore acts as a singlet oxygen sanitizer in the presence of light (non-enzymatic) (Wiking and Nielsen, 2004). In this wavelength region, significant differences (F-value and p-value) were observed only in buffalo milk (Fig. 4 B).

### 3.2.3. Effect of storage time on milk fluorescence spectra

The effect of storage time on milk fluorescence spectra reveals essential information about the quality and stability of dairy products. Fig. 5 represents changes in fluorescence intensity of tryptophan,

Vitamin A, and synchronous fluorescence spectra of each milk species during storage time (0, 24, 48, and 72 h) at 5°C. Concerning the tryptophan fluorescence spectra, a different trend was observed depending on the milk species (i.e. cow vs. buffalo). For cow milk, a gradual increase was noted in the fluorescence intensity during storage from 0 to 72 h. The raw milk (no storage) presented the lowest fluorescence intensity and spectral bandwidth, while milk stored for a longer time (72 h at 5°C) exhibited higher intensity and broader bands. A shift of the position of the maximum of the band was also observed during storage. The maximum intensity of the fluorescence band decreased during storage time from 364 nm (raw milk) to 361 nm (milk stored for 72 h). Concerning the buffalo milk, an inverse trend concerning the maximum fluorescence was observed. The highest intensity and width were observed for the raw milk, while the milk stored for 72 h presented the
lowest intensity and width. Decreases in peak intensity as well as red-shifts were previously reported during storage of dairy products (i.e. yogurt). These changes were associated with changes in the environment of the tryptophan residues in dairy proteins during storage (Becker et al., 2003). The difference in variation between the two species can tentatively be associated with differences in the milk composition and to thermal treatments previously applied to the milk samples. It was previously reported that buffalo milk undergoes more rapid denaturation of whey proteins, particularly β-lactoglobulin, compared to bovine milk (Elagamy, 2000). At 75°C, buffalo skim milk shows a greater reduction in native β-lactoglobulin—43 %—compared to only 34 % in bovine skim milk. This difference in denaturation is linked to genetic variations in the milk proteins: buffalo milk contains a β-lactoglobulin variant (similar to bovine β-lactoglobulin B) that is more prone to heat-induced changes than the dominant β-lactoglobulin A in cows (Ahmad, 2013). As heating temperature exceeds 75°C, denaturation in buffalo milk continues to increase, further emphasizing its greater sensitivity to heat than bovine milk (Mejares et al., 2022).

Concerning Vitamin A, the maxima of the three peaks varied depending on storage time. For cow milk, an increase was observed for the first peak during storage. For the second and third peaks an equivalent trend was observed during storage, with an increase during 24 h and then a decrease until the end of storage (48 and 72 h). For buffalo milk, the fluorescence intensity showed a decrease in the first peak during storage. However, an increase was observed for the other two peaks up to 48 h, followed by a decrease in the maximum intensity at 72 h. The amount of oxygen in the headspace, along with that dissolved oxygen in the milk, is known to negatively impact vitamin stability (Kim et al., 2000). Moreover, it was previously reported by Christensen et al. (2003) that the decrease in Vitamin A fluorescence intensity in dairy products during storage is due to the photolight degradation during light exposure.

Concerning synchronous fluorescence spectra, the tryptophan and Vitamin A peaks exhibited the same trends as previously observed for classical front-face fluorescence spectra described above. A different trend was observed for the riboflavin region (maximum intensity at 484 nm) depending on the milk species. A decrease in fluorescence intensity of raw milk was observed until 24 h, while an increase was observed from 48 to 72 h. For buffalo milk, when compared to raw milk, an increase was observed until 48 h, while a decrease was noted for the milk stored for 72 h.

Based on the direct spectra analysis, we observed that milk classification depending on species, heating treatment, and cold storage time is difficult. PCA applied to spectra (results not shown) did not succeed in clustering milk samples depending on their thermal treatment, and storage time. The limitation can be due to the lack of PCA to cover complex, nonlinear relationships within the data. Therefore, further investigation was carried out to discriminate between different milk classes based on the batch (batch A to C, as shown in Fig. 1) using a machine learning algorithm called MLP-ANN. This approach is promising for extracting valuable insights from intricate spectral data and improving the accuracy of classification models.

### 3.3. Discriminant analysis by MLP-ANN pattern recognition models

In the present study, various MLP-ANN models were investigated and tested. The best-performing models, selected based on their classification accuracy and mean square error (MSE), were chosen for discussion. Table 2 presents the classification accuracies, MSE values, and number of training epochs required to achieve the best model for each data batch (A to D, as referenced in Fig. 1). The classification accuracies ranged from 44 % to 100 %, depending on the fluorescence spectra used—Vitamin A, tryptophan, synchronous excitation spectrum or tryptophan-Vitamin A combination. MSE values ranged from 0 % to 14 %, and the number of training epochs varied between 16 and 90.

When considering batch A, the synchronous fluorescence spectra exhibited the highest accuracy of 94 % and the lowest MSE of 2 %. This highlighted that the discrimination of milk based on their heating treatment can be related to the different fluorophores that were identified in the synchronous fluorescence spectra (Fig. 4). The successful discrimination of milk samples based on heating treatment using synchronous fluorescence spectra can be attributed to the different biochemical and physicochemical changes that occur in milk during heating. These changes include a decrease in pH, calcium phosphate precipitation, lactose isomerization, whey proteins denaturation and their interaction with casein, Maillard browning, and casein micelle alteration (Fox et al., 2015). Furthermore, as previously reported in the literature (Zhang et al., 2021) the thermal process leads to the production of hydroxymethylfurfural (HMF), and carboxymethyl lysine, which further contribute to the complexity of milk composition and its intrinsic fluorescence properties. The molecular modifications induced by heating treatments have a direct impact on the intrinsic fluorescence of milk, reflecting the complex relationship between structural changes in milk components and the resulting fluorescence characteristics.

Different studies have been reported on the use of spectroscopy to detect molecular changes induced by heating treatments, classify milks based on their treatment history, or predict changes in milk constituents (Aït-Kaddour et al., 2021). However, until the present study, no study had reported the possibility of directly quantitatively discriminating different milk species submitted to different heating treatments using the autofluorescence of milk. In contrast, our study demonstrates that this novel approach enables precise differentiation between milk types based on their heating processes, showcasing the potential of autofluorescence.

For batch B, all the fluorophores or combinations of fluorophores were allowed to reach a value of 100 % for classification accuracy and 0 % for the MSE. Interestingly, the model required the fewest number of epochs (16) to achieve optimal training when considering the synchronous fluorescence spectra. Therefore, it can be advised to use the combination of synchronous fluorescence spectra with MLP-ANN as a valuable tool to discriminate milk species. In a previous study, Genis et al. (2020) and Hougaard et al. (2013) demonstrated that synchronous fluorescence spectra obtained with a Δλ equal to 90 nm were capable of discriminating cow milk from milk obtained from other species. The discrimination between milk samples was associated with intensity variations observed on tryptophan, Vitamin A, and riboflavin fluorescence bands (Fotakis et al., 2020). These conclusions are in accordance

<caption>**Table 2**</caption>

Best pattern recognition model's accuracy, root mean square error (MSE) and number of epochs obtained for the different batches. Vitamin A (Vit A), tryptophan (Trp), Synchronous spectra (Sync).

<table><thead><tr><th rowspan="2"></th><th colspan="4">Number of epochs</th><th colspan="4">Accuracy (%)</th><th colspan="4">MSE (%)</th></tr><tr><th>A</th><th>B</th><th>C</th><th>D</th><th>A</th><th>B</th><th>C</th><th>D</th><th>A</th><th>B</th><th>C</th><th>D</th></tr></thead><tbody><tr><td>Batch</td><td>72</td><td>16</td><td>90</td><td>82</td><td>94</td><td>100</td><td>94</td><td>100</td><td>2</td><td>0</td><td>3</td><td>2</td></tr><tr><td>Sync</td><td>66</td><td>19</td><td>86</td><td>72</td><td>75</td><td>100</td><td>94</td><td>88</td><td>5</td><td>0</td><td>2</td><td>9</td></tr><tr><td>Trp</td><td>50</td><td>85</td><td>84</td><td>16</td><td>44</td><td>100</td><td>100</td><td>56</td><td>8</td><td>0</td><td>2</td><td>13</td></tr><tr><td>Vit A</td><td>39</td><td>74</td><td>51</td><td>45</td><td>63</td><td>100</td><td>100</td><td>63</td><td>7</td><td>0</td><td>2</td><td>14</td></tr></tbody></table>
<table><caption>**Table 3**
Best MPL-ANN pattern recognition models calculated for each data batch at training, validation and test stages.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Data matrix</th><th rowspan="2">Preprocess</th><th colspan="2">Number of neurons</th><th rowspan="2">Number of epoch</th><th colspan="3">Accuracy (%)</th></tr><tr><th>First layer</th><th>Second layer</th><th>Training</th><th>Validation</th><th>Test</th></tr></thead><tbody><tr><td>Batch A</td><td>Sync</td><td>RAW</td><td>8</td><td>7</td><td>72</td><td>96</td><td>100</td><td>94</td></tr><tr><td>Batch B</td><td>Sync</td><td>RAW</td><td>4</td><td>2</td><td>16</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Batch C</td><td>Trp-Vit A</td><td>MSC</td><td>4</td><td>7</td><td>51</td><td>100</td><td>100</td><td>100</td></tr><tr><td>Batch D</td><td>Sync</td><td>AREA</td><td>7</td><td>7</td><td>82</td><td>100</td><td>99</td><td>100</td></tr></tbody></table>

Sync: matrix of synchronous spectra; Try-Vit A: concatenation of Tryptophan and Vitamin A matrices; AREA: Area under curve = 1, MSC: Multiple Scattering Correction; SNV: Standard Normal Variate.

with differences highlighted by the synchronous spectra reported in the present study (Fig. 2).

For batch C, exceptional results were achieved, with a remarkable 100 % accuracy and a minimal MSE of 2 % observed when considering Vitamin A spectra. Furthermore, by concatenating the tryptophan and Vitamin A spectra (Trp-Vit A), the discrimination model exhibited optimal performance. Notably, the model utilizing the Trp-Vit A concatenated data matrices required 51 epochs to reach the best model accuracy. This model also needed fewer neurons in the two hidden layers (4–7) to process the discrimination model. Therefore, this last ANN model was designated as the best one to discriminate milks based on their storage time. This optimized ANN model not only streamlines the data input requirements but also minimizes the computational complexity, making it a practical and effective tool for differentiating milk samples based on their storage duration.

When considering batch D, the highest discrimination accuracy (100 %) was obtained using synchronous spectra alone. The model utilizing synchronous fluorescence data exhibited an MSE of 2 % and required 82 epochs to reach optimal performance. This outcome suggests that synchronous spectra alone lead to efficient model training and strong discrimination capabilities. The sensitivity of synchronous fluorescence spectra (band shapes and intensities) to heating treatments was noted after visual inspection of spectra (Fig. 4), the model accuracy obtained when considering batch D, and corroborated by previous studies (Boubellouta and Dufour, 2008; Loudiyi et al., 2018). Considering the synchronous spectra alone as inputs for the model gave a high classification accuracy (100 %). By contrast, the combination of tryptophan and Vitamin A spectra achieved a lower classification accuracy (63 %) and higher MSE (14 %), despite requiring fewer epochs (45). This emphasizes the robustness of synchronous spectra for discriminating heat-treated milk samples (Fig. 4), as previously reported (Boubellouta and Dufour, 2008; Dufour et al., 2000). The limited performance of the Trp-Vit A combination in this batch may be attributed to the complexity of the classification task, involving multiple classes and overlapping factors (8 classes, heating and species).

Table 3 presents the optimal MLP-ANN models developed for each batch of spectral data. These models were constructed using different preprocessing methods—RAW, MSC, and AREA—highlighting the importance of selecting appropriate preprocessing techniques based on the specific classification task. This aligns with previous findings that emphasize how preprocessing choices are context-dependent and influenced by the model's objective (Aït-Kaddour et al., 2018; Mishra et al., 2020)—in this case, the number and type of milk classes to be discriminated in each batch.

The number of neurons in the first and second hidden layers varied from 4 to 8 and 2–7, respectively, while the number of training epochs ranged from 16 to 82. These variations suggest that the complexity of the neural network tends to increase with the complexity of the classification problem. For instance, Batch B (cow's milk vs buffalo's milk), which involved only two classes, resulted in the simplest MLP-ANN model. This model achieved perfect accuracy with raw spectral data and required the fewest neurons (4 in the first layer and 2 in the second layer) and the shortest training time (16 epochs), illustrating how simpler

classification tasks may demand less model complexity.

## 4. Conclusion

In this study, we employed PCA to assess the impact of thermal treatments (cooling, thermization, and pasteurization) on the physicochemical properties of buffalo and cow milk. Additionally, we explored the use of fluorescence spectroscopy—targeting Vitamin A, tryptophan, and synchronous fluorescence (Δλ = 80 nm)—combined with MLP-ANN to classify milk samples based on their thermal history.

PCA effectively clustered the samples by milk species, storage time, and heat treatment, revealing clear differences in parameters such as pH, lactose, acidity, nitrogen fractions, calcium content, and rennet clotting time. Furthermore, MLP-ANN applied to fluorescence data successfully differentiated samples according to thermal treatment, storage duration, and species, achieving a 100 % classification accuracy. These results highlight the potential of combining non-destructive fluorescence spectroscopy with machine learning to detect molecular changes in milk induced by processing and storage.

This integrated approach could provide a rapid, accurate, and chemical-free method for evaluating milk quality and processing history. Its practical relevance is especially significant for the dairy industry, where verifying the thermal history of raw milk is essential for producing products with PDO, such as traditional raw milk cheeses. The method could enable authenticity verification, supports regulatory compliance, and preserves artisanal production standards. By facilitating routine quality control and enhancing traceability, this technology may also help prevent mislabeling, reduce economic losses, and strengthen consumer confidence in dairy products.

### CRediT authorship contribution statement

**Abdelmentolb Hani:** Writing – original draft, Validation, Investigation, Formal analysis, Data curation, Conceptualization. **Abou-El-Karam Said:** Writing – review & editing, Software, Data curation. **Hamdy Shaimaa:** Writing – original draft, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Abdelbaky Hassan:** Writing – original draft, Resources, Project administration, Methodology, Conceptualization. **Tahar Boubellouta:** Visualization. **AIT KADDOUR Abderrahmane:** Writing – review & editing, Writing – original draft, Validation, Software, Formal analysis, Data curation.

### 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

The data that has been used is confidential.
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