# Assessment of biophysical properties of mango purées from whole-fruit NIRS of different varieties

Julien Ricci, Nathan Tanguy, Camille Demoulin, Lidwine Grosmaire, Simon Carlier, Doralice Veillant, Fabrice Davrieux, Mathieu Lechaudel, Layal Dahdouh

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Julien Ricci, Nathan Tanguy, Camille Demoulin, Lidwine Grosmaire, Simon Carlier, et al.. Assessment of biophysical properties of mango purées from whole-fruit NIRS of different varieties. LWT - Food Science and Technology, 2026, 240, pp.118917. 10.1016/j.lwt.2025.118917. hal-05480875

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# Assessment of biophysical properties of mango purées from whole-fruit NIRS of different varieties

Julien Ricci<sup>a,b</sup>, Nathan Tanguy<sup>a,b</sup>, Camille Demoulin<sup>a,b</sup>, Lidwine Grosmaire<sup>b</sup>, Simon Carlier<sup>a,b</sup>, Doralice Veillant<sup>c</sup>, Fabrice Davrieux<sup>a,b</sup>, Mathieu Léchaudel<sup>a,b</sup>, Layal Dahdouh<sup>a,b,*</sup>

## ARTICLE INFO

Keywords:

Mango purées

Biophysical properties

Near-infrared spectroscopy

PLS models

Quality prediction

## ABSTRACT

This work combined multi-variety, multi-ripening stages with non-destructive Near-Infrared Spectra (NIRS) modeling to predict processed mango purée quality traits. 2083 fruits were analyzed by non-destructive texture and NIRS measurements, and then processed into 240 purées. The quality traits of 61 representative purées were characterized. The performance of partial least-square regression (PLS) models was evaluated to predict the purées' dry matter (DM), total soluble solids (TSS), pH, titratable acidity (TA), color (L*, a*, b*), rheological parameters (flow indices n, consistency index K, flow point γf, elastic and viscous moduli (G'LVE and G"LVE)), and granulometric parameters (statistical diameters D [3; 2] and D [4; 3]). Cogshall purées had the highest n, and the lowest G'LVE, G"LVE and D [3; 2] values. Modelling highlighted that ripening was the only factor modulating D [3; 2], whereas γf was variety-dependent. The fruit NIRS PLS models with RPD value > 2.5 for DM, G'LVE, while G"LVE and γf demonstrated good/excellent performance. For TSS, b*, D [3;2] and D [4;3] satisfactory quantitative prediction was obtained (2 < RPD <2.5). Finally, NIRS presented poor predictive performance for pH, AT, L*, a*, n and k. These findings should be extended to other varieties and production systems to achieve higher representativity and genericity.

## 1. Introduction

The mango (*Mangifera indica* L.) is a tropical fruit originating from the Indo-Burmese region. This fruit is one of the most consumed tropical fruits worldwide (Evans et al., 2017; Parvez, 2016; Tiyayon & Paull, 2017). The mango is appreciated for its flavors, and its richness in bioactive compounds such as vitamins, minerals, and antioxidants. However, the fruit's seasonal production and vulnerability to disease reduce its availability, particularly during the off-season. Hence, the processed forms of this fruit have become established in the agri-food industrial sector. The processed products offer many advantages, such as more regular availability, longer shelf life, and an organoleptic profile as appealing as that of the fresh fruit. Examples include purées, juices, jams, canned products, and dried slices (Evans et al., 2017).

Mango purée remains by far the most important processed mango product, due to its use whether as a final product or as an ingredient in the formulation of a variety of other products. Mastering purée quality is therefore necessary in order to meet both consumer and agri-food

industry requirements. Studies have shown that fruits purée quality is strongly dependent on the biochemical, physico-chemical, and physical properties of the raw material (Büergy, 2021; Lan, 2021). In the case of mango, the biophysical quality of purées is also dependent on fruit ripening stage and firmness, since the mango is a climacteric fruit, i.e., it continues its ripening process after harvesting (Ellong et al., 2015; Labaky et al., 2020, 2021). In addition, the varietal diversity (Djioua, 2010) of the mango, with varieties that differ not only in production origin and seasonality, but also in contrasting physiological, nutritional, and organoleptic properties, makes mastery of mango purée quality in processing units even more complex. Moreover, the heterogeneity of the ripening stages encountered at harvest in a single mango batch intended for processing constitutes a real challenge for quality control of the processed products (Rivier et al., 2009).

While multiple tools have been evaluated for predicting raw mango maturity and some of its quality traits (Pronprasit & Natwichai, 2013; Valente et al., 2004; Zakaria et al., 2012), none have yet been designed able to accurately predict the quality of processed mango products.

<footnote><sup>a</sup> CIRAD, UMR Qualisud, Saint-Pierre, F-97410, France</footnote>

<footnote><sup>b</sup> Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France</footnote>

<footnote><sup>c</sup> CIRAD, UPR Hortsys, F-97455, Montpellier, France</footnote>

<footnote>* Corresponding author. CIRAD, UMR Qualisud, Saint-Pierre, F-97410, France.
E-mail address: layal.dahdouh@cirad.fr (L. Dahdouh).</footnote>
However, several studies have demonstrated the feasibility and usefulness of using near-infrared spectroscopy in food sectors as a process analytical technology for evaluating specifically apple purée variability (Lan et al., 2020), and the quality of several foods such as milk, fish, and

fish products (He et al., 2015; Strani et al., 2021). When applied to whole fruits, NIRS enables rapid, non-destructive, and reagent-free measurement of internal attributes (total soluble solids content, dry matter, firmness, etc.), allowing individual sample inspection and

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Experimental strategy for (i) mango sorting based on non-destructive texture and NIRS measurements, (ii) fruits processing into purées, (iii) purées selection for characterization, (iv) purées quality traits characterization, and (v) NIRS modelling prediction.</figcaption></figure>
continuous sorting, while reducing waste and analytical costs (Walsh et al., 2020). Advances in chemometrics and deep learning have strengthened the model robustness, and facilitated integration in the field and on the production line (Jiang et al., 2025). As far as the authors are aware, no studies have yet investigated the possibility of using near-infrared spectra of mango fruits to characterize the quality of mango purées. Only a few studies have addressed the issue of predicting mango purée quality, and hence have evaluated the potential of using non-destructive tools to assess mango purée quality based on fruit properties. For example, Labaky et al. (2021), demonstrated the potential of using simultaneous rheometry and in-situ FTIR spectroscopy for rapid identification of biophysical changes in mango purées, though without taking into account the contribution of variety to purée quality, or developing a quantitative predictive approach. They also showed that fruit firmness could be a good indicator of purée quality, highlighting the crucial role of ripening stage in the biophysical properties of purées.

In light of all this knowledge, joint consideration of the ripening stage and mango variety in a predictive approach constitutes an interesting approach to mastering mango purée quality. This study aimed to: (i) understand the joint role of fruit variety and ripening in processed purée quality, and (ii) evaluate the feasibility of predicting mango purée quality (rheological, particle size, and physico-chemical properties) based on whole-fruit NIRS data. Hence, to explore the potential of an NIRS predictive approach in the field of mango processing, an experimental strategy was designed in this work in order to characterize and predict, in a novel way, the quality traits of mango purées across multiple varieties and ripening stages. This integrative design distinguishes the work from prior fruit NIRS studies, which have commonly focused on raw fruit maturity alone. Addressing this challenge would expand the very limited literature on this subject, and open up new possibilities for producing, on an industrial scale, purées with targeted and controlled quality.

# 2. Materials and Methods

## 2.1. Mango fruits

The fruits were harvested from CIRAD's experimental mango orchards located in the Saint-Pierre district of Reunion Island, France. Three mango varieties were used in this work (Mangifera indica L., cv. José, cv. Cogshall, and cv. Tommy Atkins), because of their contrasting biophysical and physiological properties, with a set of 2083 fruits in total.

## 2.2. Experimental strategy

To obtain purées with different rheo-physical properties, a sorting strategy (Fig. 1) was adopted prior to purée production, to obtain fruit batches distinguished by their varietal origin and ripening stages, based on firmness. After harvesting, the firmness of each fruit was measured using a non-destructive compression test (Labaky et al., 2020). Then, depending on their firmness, the fruits were either processed into purées on the day of harvesting, or stored under controlled temperature and relative humidity conditions (20 °C ± 0.5 °C, 80 % RH ± 5 % RH with storage time varying between 1 and 15 days) to achieve a target firmness before processing. According to this protocol, it was possible to group together fruits with similar firmness and from the same variety in the same batch. Hence, 240 batches each comprising 5 to 12 fruits with similar firmness were obtained. All the batches were processed, and 240 purées were obtained. Under these conditions a constant supply of mango pulp was provided for mango purée production. On the day the purées were prepared, near-infrared spectra were collected on the unpeeled fruits before processing.

## 2.3. Non-destructive measurements on whole unpeeled fruits

### 2.3.1. Firmness measurement

The compression test developed by Labaky et al. (2020) was used in this work to perform non-destructive evaluation of mango fruit firmness. This test was proposed by the authors to simulate the tactile perception of the operator during the process of sorting mango fruits by firmness. In this study, the measurements were taken, at room temperature, on both sides of the fruit. The test was carried out with a calibrated texturometer (TA-XT plus model, Stable Micro Systems, Godalming, UK) equipped with a 50 kg load cell and a 2 cm stainless steel spherical probe, to reach a maximum compression force of 5N at a probe speed of 1 mm.s⁻¹. The obtained force-distance curves were recorded, and the distances reached for a compression force of 5N (D5N) was identified as an indicator of mango firmness. According to their specific D5N values, the fruits were sorted into 240 batches to be processed into 240 distinct purées, as described in Fig. 1.

### 2.3.2. NIR spectral acquisition

The Vis-NIRS spectral measurements were taken on unpeeled fruits, with spectral data acquisition at room temperature in diffuse reflectance using a Labspec 5000 spectrometer equipped with a contact probe (ASD Inc., Malvern Panalytical, Malvern, UK). The spectra were acquired at wavelengths between 350 nm and 2500 nm, with a spectral resolution of 10 nm and a step size of 1 nm. For each spectrum, 10 scans were recorded and averaged, and to calibrate the equipment the zero baseline was obtained by measuring the reference reflectance (Spectralon). The spectral acquisition and instrument monitoring operations were carried out using Indico-Pro software (Analytical Spectral Devices Inc., Boulder, CO). Due to the heterogeneous sun exposure of the fruits and its influence on color and composition, spectra were collected on both sides of the fruits (sun-exposed and shaded). The measurement was taken in duplicate on the central position of each side since this was the most representative area in terms of light exposure of the fruit within its environment. A total of 4166 spectra were acquired for 2083 fruits, previously water washed and gently dried.

## 2.4. Purée processing

The mangos within the same batch were manually peeled, deseeded, and the flesh cut into small pieces (around 3.5 cm³). The flesh obtained was homogenized and weighed to ensure a batch of 1 kg. The fruit pieces were then ground at 25 °C with a Thermomix (Vorwerk, Type 31, Wuppertal, Germany) at a speed of 1100 rpm for 2 min (homogenized after 1 min of grinding), then at 4400 rpm for 1 min and finally at 10200 rpm for 1 min. After grinding, the purées were stored at -20 °C and thawed at 30 °C for 60 min immediately before characterization.

## 2.5. Purée characterization

### 2.5.1. Purée selection for characterization

This selection aimed to reduce the number of mango purées to be characterized for each variety. To achieve this, the most representative purées were selected based on the NIR spectral fingerprint of the raw average spectrum. Firstly, a first principal component analysis (PCA) was performed on mean-centered spectral data. Then the Kennard-Stone algorithm (Kennard & Stone, 1969; Nawar & Mouazen, 2018), using the Mahalanobis distance as the selection metric, was applied to the scores of the first five principal components of this PCA. The total number of selected purées was set at 61 of the 240, i.e., at least 20 per variety.

### 2.5.2. Conventional physico-chemical analyses

Titratable acidity (TA) and total soluble solids content (TSS) were measured on the mango purées according to the protocols and methods used for fruit suspensions (Dahdouh et al., 2016). The pH and titratable acidity were measured with a titrometer (TitroLine easy, Schott Geräde
GmbH, Mainz, Germany). The titration process used a 0.025 mol·L⁻¹ NaOH solution to reach a pH of 8.2. The titratable acidity was expressed as citric acid equivalent (citric acid eq. g·L⁻¹), which is the major organic acid in the mango. The total soluble solids content (TSS, °Bx) was measured with a refractometer (HI 96801, Hanna Instrument, Szeged, Hungary). The total dry matter (DM, g/100 g equivalent to 10⁻² g/g in SI units) was obtained by drying 3 g of the samples at 50 °C for 48 h, and then at 70 °C for 48 h. For all the physico-chemical analyses, the measurements were taken at 25 °C in triplicate, and the average values were used. The color of the samples was measured, on flat purée surface, using a chromameter (CR-400, Konica Minolta, Osaka, Japan), equipped with a CR-A33d measuring head (8 mm aperture), with a D65 illuminant and d/0° measurement geometry, after calibration with a standard white plate. The results were expressed in the CIE L*a*b* color space, with L* corresponding to the lightness between 0 (white) and 100 (black), a* indicating the variation from red (−) to green (+), and b* the variation from blue (−) to yellow (+).

### 2.5.3. Rheological measurements

To characterize the viscoelastic and flow behavior of the mango purées, rotational, and oscillatory strain sweep measurements were taken using a Physica MCR 301 controlled stress rheometer (Anton Paar GmbH, Graz, Austria). The equipment was coupled to a thermal control system (VISCOTHERM VT2, Anton Paar GmbH, Graz, Austria) in order to take the measurements at a controlled temperature of 25 °C. The measurements were taken with a coaxial cylinder geometry and an air gap of 5 mm. This geometry consists of a rotor (CC 18.92–40/118/S, Anton Paar GmbH, Graz, Austria) with a diameter of 18.9 mm, and a fixed cup (stator) with a diameter of 28.9 mm. The StartRheoplus software was used for data collection (RHEOPLUS/32 V3.40, Anton Paar GmbH, Ostfildern, Germany). All the trials were repeated at least twice to ensure the consistency of the results.

#### 2.5.3.1. Rotational measurements – flow behavior.
After a pre-resting period of 30 s, the viscosity was measured at a controlled shear rate range varying between 0.1 and 500 s⁻¹, with 19 measurement points at no mode setting. The evolution of dynamic viscosity μ (Pa·s) as function of the shear rate ˙γ (s⁻¹) was then recorded. The mango purées can be said to characterized by shear-thinning behavior, since their viscosity decreased with increasing shear rate - which is in accordance with literature (Espinosa-Muñoz et al., 2013; Gundurao et al., 2011; Labaky et al., 2020, 2021; Phaokuntha et al., 2014). Therefore a power law (specific for this behavior) was used to determine the consistency index (K, Pa·sⁿ) and the flow behavior index (n) of each purée, Eq. (1):

$$ \text{Power law: } \mu_{\text{mod}} = K \dot{\gamma}^{n-1} \quad (1) $$

Where μmod is the modelled viscosity (Pa·s), K is the consistency index (Pa·sⁿ), n is the flow behavior index, and ˙γ is the shear rate (s⁻¹).

#### 2.5.3.2. Oscillatory strain amplitude sweep measurements – viscoelastic behavior.
The purée samples were first subjected to a pre-shearing period of 30 s at 50 s⁻¹, followed by a relaxation period of 120 s and a strain adjustment period at 0.01 % for 60 s. Finally, the strain sweep tests were performed using a strain γ (%) variation from 0.01 to 500 % at a constant frequency of 1 Hz. The evolution of storage G' (Pa) and loss G'' (Pa) moduli as function of strain γ (%) was recorded. The storage and loss moduli G'_{LVE} and G''_{LVE} in the linear viscoelastic region (LVR) were determined, and used as rheological indicators reflecting (i) the solid-like behavior (representing the material's stiffness and the energy recovered when the stress is released) and (ii) liquid-like behavior (representing the energy lost through friction, and molecular and particle motion) of the mango purées, respectively. The linear viscoelastic (LVE) region of each sample was determined with an LVE limit defined by the strain causing a 5 % reduction in modulus values (Kaboorani & Blanchet, 2014). The shear strain γf (%) value corresponding to the flow

point where the storage and the loss moduli were equal (G' = G'') was also recorded.

### 2.5.4. Particle size measurements

Suspended-particle size distribution measurements were taken by laser diffraction using a Malvern Mastersizer (Mastersizer 3000, Malvern Instruments Limited, Worcestershire, UK). This particle size analyzer can theoretically provide a particle size distribution from 10 nm to 3500 µm. The measurements were taken in wet mode, using distilled water as the suspension medium. Values of 1.73 and 1.33 were used for the refractive indices of cloud particles and dispersion phase (water), respectively. A value of 0.1 was used for the absorption index of cloud particles (Dahdouh et al., 2016; Demoulin et al., 2022, 2023; Labaky et al., 2021). The obscuration was set between 20 % and 30 %, depending on the sample properties. During the measurements, the diluted sample was stirred at 1000 rpm and pumped through the optical cell. All the measurements were taken at least in triplicate, to ensure the robustness of the results. The surface area mean diameter D [3;2] (Sauter mean diameter, µm) and the volume mean diameter D [4;3] (De Brouckere mean diameter, µm) were used as particle size distribution indicators. These diameters are conventionally used to describe the particle size distribution of fruit suspensions (Dahdouh et al., 2015; Demoulin, Wisniewski, et al., 2023; Labaky et al., 2020).

D [3;2] indicates the diameter of a sphere that has the same volume/surface ratio as the set of particles, Eq. (2):

$$ D [3;2] = \frac{\sum_i n_i d_i^3}{\sum_i n_i d_i^2} \quad (2) $$

D [4;3] indicates the diameter of a sphere whose volume is equal to the mean volumes of all the particles in the sample, Eq. (3):

$$ D [4;3] = \frac{\sum_i n_i d_i^4}{\sum_i n_i d_i^3} \quad (3) $$

where nᵢ is the number of particles of diameter dᵢ and D [3;2] and D [4;3] are expressed in µm.

### 2.5.5. Statistical analyses of reference data

Data statistical analyses were carried out using the Rstudio software (RStudio 2024.12.1 + 563, RStudio, PBC, Boston, MA, USA) and R (version 4.4.2, R Foundation for Statistical Computing, Vienna, Austria). The reference data of the processed purées were presented as mean values, and the data dispersion within the present experimental dataset was expressed as standard deviation values (SD). Principal component analysis (PCA) is a multivariate projection method designed to reduce the dimensionality and to describe the data variation (Nur Azira et al., 2014). PCA was performed on all the reference data from the three mango purée varieties to assess their variability in terms of biophysical descriptors, and to distinguish differences attributable to variety. Thirteen purée quality indicators were selected as variables (particle size: D [3;2], D [4;3], rheological behavior: G'_{LVE}, G''_{LVE}, γf, K, n, and physico-chemical characteristics: TA, TSS, DM, L*, a*, and b*). The score plot was extracted from the first three principal components, PC 1, PC 2, and PC 3, since they presented the most data variability.

Non-linear regressions were performed with the Rstudio software, using the nlme package. The relationships between physico-chemical descriptors and firmness (D₅N) were modelled using several non-linear models (asymptotic, exponential, logarithmic, and Michaelis Menten). The selection of the optimal model was only based on the AIC (Akaike information criterion) and BIC (Bayesian information criterion), which evaluate the quality of fit by taking into account the model complexity (number of parameters). In addition, ANOVA comparison tests were performed to compare the model with and without variety. The normality of the residuals was evaluated using a quantile-quantile plot, and confirmed with a Shapiro-Wilk test. The homoscedasticity of variances was visually examined using a residual prediction scatter, and
statistically verified using Levene's test. For each variety, the parameters of the final model were compared statistically by peer comparison with a Holm adjustment to control the standard error rate in the comparisons. The coefficient of determination (R²) and root mean square error (RMSE) were calculated to assess the accuracy of the models.

### 2.5.6. Chemometric analysis

Spectral pre-processing and multivariate regression were carried out using the Rchemo package in the Rstudio software (RStudio 2024.12.1 + 563, RStudio, PBC, Boston, MA, USA) (Brandolini-Bunlon et al., 2024).

#### 2.5.6.1. Spectral pre-processing.
Spectral pre-processing is commonly used to improve the quality of spectral data by eliminating irrelevant information which can lead to non-performing chemometric models. Firstly, the spectral data were trimmed to the range 500–2250 nm in order to eliminate edge effects and high-noise zones. Secondly, various mathematical spectral pre-processing methods were evaluated to improve the calibration models (Barnes et al., 1989; Savitzky & Golay, 1964): Savitzky-Golay (SG) smoothing, second derivative Savitzky-Golay (SG2) with second-order polynomial regression, standard normal variate (SNV) transformation, and various combinations of these pre-processing methods. The Savitzky-Golay filter was applied, with the following parameters: for the first derivative (SG1), derivative order $m=1$, window size $n=13$ points, and polynomial order $p=2$; for the second derivative (SG2), derivative order $m=2$, window size $n=21$ points, and polynomial order $p=2$.

#### 2.5.6.2. Partial least squares regression.
Partial least square (PLS) regression models were developed in this study to predict the physicochemical properties of mango purées based on the acquired Vis-NIR spectra on unpeeled mango fruits. The set of all the spectra was split into calibration and external validation sets using the Kennard-Stone algorithm stratified by variety. A calibration: validation ratio of 70:30 was adopted for the spectra for each mango variety. To avoid overfitting, a K-fold cross-validation was performed on the calibration to determine the optimal number of latent variables (LVs) for PLS models for each biophysical indicator. For each indicator, the procedure was repeated 50 times with a K of 4, and the root mean square error of cross-validation (RMSECV) was used as an indicator of the cross-validation performance. Then, the prediction performances of the calibrated PLS models were evaluated with the external validation. These regression models were evaluated according to their calibration coefficient of determination ($R_c^2$), prediction coefficient of determination ($R_p^2$), root mean square error of calibration (RMSEC), and the ratio of performance deviation (RPD). Thus, the selection of the best model was based on a combination of the best RMSECV, RMSEP, and RPD values, and the simplicity of latent variables (LVs). RPD values between 1.5 and 2 indicate a model which can distinguish between low and high values of the response variable, values between 2 and 2.5 indicate that quantitative prediction is possible, and values between 2.5 and 3 or more correspond to good or excellent predictive performance (Nicolaï et al., 2007; Williams, 1987, pp. 143–167). In addition, to quantify the uncertainty and robustness of model performance metrics, a bootstrap resampling procedure (B = 1000 iterations) was performed on the external validation dataset. For each bootstrap replicate, random sampling with replacement was applied to the validation samples, and the RMSEP and $R^2_p$ values were recalculated. The distributions of these metrics were then used to estimate 95 % confidence intervals (CI95 %) for RMSEP and $R^2_p$, as well as their standard errors. This approach provides a more reliable estimate of model generalization performance by accounting for sampling variability in the validation dataset. Only the pre-processing methods and variable selection algorithm with the best predictive performance are presented in the results.

## 3. Results and discussion

### 3.1. Physico-chemical and physical characterization of mango purées

The results regarding the physico-chemical, particle size, and rheological properties of the mango purées obtained from three mango varieties and different ripening stages are presented in Fig. 2—and the range values are reported in Table 2. A color gradient reflecting different D5N values is used to indicate different ripening stages. Indeed, the work of Labaky et al. (2020), showed that mango firmness measured by a non-destructive compression test is an excellent indicator of the fruit's ripening stage. Early-ripening fruits have low D5N values (distance covered by the texturometer probe for an applied force of 5N), and these values increase as the fruit softens during ripening, reaching higher values. The analysis of dry matter (DM), total soluble solids content (TSS), pH, and titratable acidity (TA) values showed that purées obtained from the José variety had globally higher DM, TSS, and pH values, as well as lower TA and $L^*$ values compared to the other two varieties. Conversely, purées from the Tommy Atkins variety were characterized mainly by their lower TSS and their higher $L^*$ and $b^*$ parameters, indicating a lighter color. Meanwhile, purées derived from Cogshall fruits were distinguished by their specific particle size and rheological properties. Indeed, these purées had the finest particle size, with D [3;2] ranging between 17 and 130 µm, and D [4;3] ranging between 106 and 357 µm — considerably lower than the ranges observed for the José and Tommy Atkins varieties. The statistical diameters characterizing the particle size of Cogshall purées are consistent with those reported in the literature (Labaky et al., 2021). Furthermore, Cogshall purées had the lowest consistency and solid-like behavior, with a consistency index K and storage modulus $G^*_{LVE}$ clearly lower than the other purées. Conversely, the shear strain $\gamma_f$ (%) measured for these purées seemed to be higher than for José and Tommy Atkins purées, indicating that a higher strain is needed to switch from a solid-like behavior to a viscous-like behavior, and to induce the purée flow. These observations can be explained by the very different physical properties of the fruits before grinding, and this is linked to the variety, since the same grinding operating conditions were adopted for all 3 varieties.

In addition, a similar effect of ripening can be observed on the disparity of physico-chemical parameter values within the same variety, across the varieties studied.—except for TA, which seems to vary very little during ripening for the José variety. However, there is a greater disparity in particle size within the Tommy Atkins and José varieties for different ripening stages, particularly for D[4;3]. Similarly, a greater disparity can be observed in the rheological indicator values for Tommy Atkins purées with ripening, particularly for consistency index K and storage modulus $G^*_{LVE}$. This indicates that the solid behavior and the consistency of these purées are strongly linked to the physical properties of the fruits. These observations can be explained by the different physical properties of the fruits before grinding, and this is linked to the variety, since the same grinding operating conditions were adopted for all 3 varieties. This underlines the role of fruit ripening in the purée properties, particularly rheological and granulometric properties, and this role is more marked in the case of the Tommy Atkins variety.

To study the role of variety and ripening stages jointly, a Principal Component Analysis (PCA) was firstly carried out for an overall analysis, and to visualize the total variability of the physico-chemical and physical characteristics of the mango purées in relation to variety and ripening stage. PCA is a statistical technique used to reduce data dimensionality while maximizing the explained variance. This analysis helps identify the main axes (principal components) that explain the majority of the variance in a multivariate dataset. As illustrated in Fig. 3, 66.1 % of the variability of the data was explained by the first two components (PC1 and PC2), and only 10.7 % by PC3 (not shown). The first component (PC1 - 33.3 %) was globally positively correlated with some physico-chemical parameters (DM, pH, TSS, and $a^*$), and one rheological indicator - the shear strain $\gamma_f$ (%). It was also negatively
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** physico-chemical, color, particle size and rheological quality traits of mango purées samples obtained from three mango varieties. a*: red to green transition, b*: blue to yellow transition, DM: dry matter (g/100 g puree), D [3;2]: Sauter mean diameter (µm), D [4;3]: De Brouckere mean diameter (µm), G'_{LVE}: storage modulus (Pa), G''_{LVE}: loss modulus (Pa), k: consistency index (Pa.s^n), L*: lightness (black to white transition), n: flow behavior index (/), pH (/), TA: titratable acidity (g citric acid/100 g purée), TSS: total soluble solids (°Bx), γf: shear strain at flow point (%).</figcaption></figure>

<caption>**Table 1**</caption>

Optimal non-linear model equation (asymptotic, logarithmic, Michaelis Menten) used to describe relationships between physico-chemical, color, particles size and rheological mango purée traits and whole fruit firmness (D5N).

<table><thead><tr><th></th><th></th><th></th><th>α</th><th>β</th><th>ω</th><th>R<sup>2</sup></th><th>RMSE</th></tr></thead><tbody><tr><td rowspan="3">TSS (°Bx)</td><td>Cogshall (n = 21)</td><td rowspan="3">Asymptotic</td><td>17.95<sup>b</sup> [17.34; 18.55]<sup>*</sup></td><td>-58.73<sup>a</sup> [-320.55; 203.09]</td><td>1.59<sup>a</sup> [0.53; 2.65]</td><td>0.38</td><td>1.27</td></tr><tr><td>José (n = 20)</td><td>α+(β-α)·exp(-exp(ω))</td><td>21.06<sup>a</sup> [20.40; 21.71]</td><td>-1.90<sup>a</sup> [-41.66; 37.87]</td><td>1.28<sup>a</sup> [0.34-2.21]</td><td>0.57</td><td>1.05</td></tr><tr><td>Tommy Atkins (n = 20)</td><td>x)</td><td>15.80<sup>c</sup> [15.14; 16.45]</td><td>-38.65<sup>a</sup> [-91.41; 14.11]</td><td>1.71<sup>a</sup> [1.27; 2.14]</td><td>0.92</td><td>0.60</td></tr><tr><td rowspan="3">L*(/)</td><td>Cogshall (n = 21)</td><td rowspan="3">Michaelis-Menten</td><td>43.09<sup>b</sup> [40.15; 46.03]</td><td>-0.23<sup>b</sup> [-0.28; -0.17]</td><td></td><td>0.88</td><td>2.42</td></tr><tr><td>José (n = 20)</td><td>α·x</td><td>40.17<sup>b</sup> [37.39; 42.95]</td><td>-0.18<sup>b</sup> [-0.23; 0.14]</td><td></td><td>0.82</td><td>3.13</td></tr><tr><td>Tommy Atkins (n = 20)</td><td>β+x</td><td>50.45<sup>a</sup> [47.17; 53.73]</td><td>-0.07<sup>a</sup> [-0.11; -0.03]</td><td></td><td>0.17</td><td>6.90</td></tr><tr><td rowspan="3">D [3;2] (µm)</td><td>Cogshall (n = 21)</td><td rowspan="3">Asymptotic</td><td>28.29<sup>a</sup> [8.79; 47.79]</td><td>224.01 [125.44; 322.57]</td><td>0.22<sup>a</sup> [-0.44; 0.88]</td><td>0.76</td><td>13.52</td></tr><tr><td>José (n = 20)</td><td>α+(β-α)·exp(-exp(ω))</td><td></td><td></td><td>0.42</td><td>52.96</td></tr><tr><td>Tommy Atkins (n = 20)</td><td>x)</td><td></td><td></td><td>0.77</td><td>23.65</td></tr><tr><td rowspan="3">G'_{LVE} (Pa)</td><td>Cogshall (n = 21)</td><td rowspan="3">Asymptotic</td><td>67.75<sup>b</sup> [27.72; 107.79]</td><td>7537.64<sup>ab</sup> [-163985.44; 179060.72]</td><td>2.05<sup>a</sup> [-2.65; 6.74]</td><td>0.20</td><td>31.35</td></tr><tr><td>José (n = 20)</td><td>α+(β-α)·exp(-exp(ω))</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>x)</td><td>255.64<sup>a</sup> [161.49; 349.78]</td><td>487.10<sup>b</sup> [152.27; 821.97]</td><td>0.03<sup>a</sup> [1.63; 2.24]</td><td>0.25</td><td>76.28</td></tr><tr><td rowspan="3">γf (%)</td><td>Tommy Atkins (n = 20)</td><td rowspan="3">Logarithmic</td><td>126.33<sup>b</sup> [80.59; 172.06]</td><td>9538.38<sup>a</sup> [2604.44; 16472.32]</td><td>1.91<sup>a</sup> [1.63; 2.19]</td><td>0.84</td><td>96.00</td></tr><tr><td>Cogshall (n = 21)</td><td>17.97<sup>a</sup> [14.99; 20.94]</td><td>9.07<sup>a</sup> [6.03; 12.10]</td><td></td><td>0.84</td><td>5.83</td></tr><tr><td>José (n = 20)</td><td>α log(x) + β</td><td>7.46<sup>b</sup> [4.00; 10.92]</td><td>10.97<sup>a</sup> [8.10; 13.84]</td><td></td><td>0.50</td><td>4.99</td></tr><tr><td></td><td>Tommy Atkins (n = 20)</td><td></td><td>1.36<sup>c</sup> [-1.19; 3.91]</td><td>8.13<sup>a</sup> [5.71; 10.56]</td><td></td><td>0.11</td><td>3.44</td></tr></tbody></table>

correlated with some physico-chemical parameters, namely color (L*, b*) and titratable acidity (TA). As for PC2, it is positively correlated with rheological parameters that reflect viscoelastic properties (G''_{LVE} and

G''_{LVE}), as well as flow behavior, namely the consistency index K. It is also positively correlated with the statistical diameters that describe the particle size of the purées (D[3;2] and D[4;3]). PC2 is negatively

<footnote n="1">¹Values in brackets indicate 95 % confidence intervals.</footnote>

<footnote>ii For each parameter, the different letters (a-c) following the non-linear regression coefficients (α, β, Ω) indicates significant difference among varieties according to pairwise comparisons (Holm-adjusted t-tests), at p < 0.05.</footnote>
<table><caption>**Table 2**

Calibration and prediction results by PLS for purée quality traits from the Vis-NIRS spectra of unpeeled mango fruits, estimated by bootstrapping.</caption><thead><tr><th rowspan="2">Parameters</th><th rowspan="2">Pretreatments</th><th rowspan="2">LVs</th><th colspan="2">Calibration (n = 43)</th><th colspan="2">Validation (n = 18)</th><th rowspan="2">Bias</th><th rowspan="2">RPD<sup>a</sup></th><th rowspan="2">range</th><th rowspan="2">SD</th></tr><tr><th>R<sup>2</sup></th><th>RMSEC</th><th>R<sup>2</sup></th><th>RMSEP</th></tr></thead><tbody><tr><td>DM (g.100g<sup>-1</sup>)</td><td>SGSNV</td><td>9</td><td>0.92</td><td>0.72</td><td>0.94 [0.89; 0.97]<sup>b</sup></td><td>0.63 [0.45; 0.79]</td><td>0.03</td><td>4.17</td><td>15.87-24.93</td><td>2.63</td></tr><tr><td>pH (/)</td><td>SNV</td><td>4</td><td>0.45</td><td>0.41</td><td>0.49 [0.14; 0.70]</td><td>0.40 [0.28; 0.51]</td><td>-0.06</td><td>1.45</td><td>3.24-5.65</td><td>0.58</td></tr><tr><td>TSS (°Bx)</td><td>SGSNV</td><td>5</td><td>0.78</td><td>1.39</td><td>0.75 [0.48; 0.94]</td><td>1.32 [0.73; 1.93]</td><td>-0.08</td><td>2.30</td><td>8.33-22.40</td><td>3.03</td></tr><tr><td>TA (eq. citric acid g.L<sup>-1</sup>)</td><td>SG2SNV</td><td>2</td><td>0.53</td><td>1.64</td><td>0.09 [0.00; 0.67]</td><td>1.35 [0.96; 1.74]</td><td>0.33</td><td>1.70</td><td>1.43-10.67</td><td>2.29</td></tr><tr><td>L*(/)</td><td>SG2SNV</td><td>5</td><td>0.74</td><td>4.16</td><td>0.46 [0.00; 0.86]</td><td>4.50 [2.81; 6.37]</td><td>1.84</td><td>1.77</td><td>40.08-71.53</td><td>7.96</td></tr><tr><td>a*(/)</td><td>SG2SNV</td><td>3</td><td>0.76</td><td>1.49</td><td>0.46 [0.00; 0.76]</td><td>1.73 [1.31; 2.10]</td><td>0.35</td><td>1.71</td><td>-3.12-7.73</td><td>2.96</td></tr><tr><td>b*(/)</td><td>SNV</td><td>4</td><td>0.56</td><td>6.18</td><td>0.66 [0.40-0.82]</td><td>4.44 [3.06-5.64]</td><td>0.66</td><td>2.07</td><td>30.62-69.50</td><td>9.20</td></tr><tr><td>log(D[3;2]) (µm)</td><td>None</td><td>4</td><td>0.76</td><td>0.34</td><td>0.78 [0.66; 0.85]</td><td>0.27 [0.20; 0.33]</td><td>-0.08</td><td>2.48</td><td>2.76-5.83</td><td>0.67</td></tr><tr><td>log(D[4;3]) (µm)</td><td>None</td><td>5</td><td>0.58</td><td>0.33</td><td>0.54 [0.00; 0.85]</td><td>0.21 [0.12; 0.31]</td><td>-0.08</td><td>2.23</td><td>4.67-6.79</td><td>0.47</td></tr><tr><td>n (/)</td><td>SNV</td><td>8</td><td>0.74</td><td>0.03</td><td>0.10 [0.00; 0.48]</td><td>0.04 [0.02; 0.05]</td><td>0.00</td><td>1.25</td><td>0.09-0.38</td><td>0.05</td></tr><tr><td>K (Pa.s<sup>b</sup>)</td><td>SG2SNV</td><td>5</td><td>0.78</td><td>5.23</td><td>0.31 [0.00; 0.68]</td><td>7.50 [5.07; 9.81]</td><td>0.17</td><td>1.45</td><td>2.76-53.48</td><td>10.84</td></tr><tr><td>log(G'<sub>LVE</sub>)(Pa)</td><td>SNV</td><td>8</td><td>0.80</td><td>0.37</td><td>0.77 [0.48; 0.93]</td><td>0.30 [0.21; 0.39]</td><td>0.08</td><td>2.66</td><td>3.34-6.89</td><td>0.80</td></tr><tr><td>log(G''<sub>LVE</sub>)(Pa)</td><td>SNV</td><td>8</td><td>0.85</td><td>0.30</td><td>0.79 [0.59; 0.91]</td><td>0.30 [0.21; 0.39]</td><td>0.04</td><td>2.60</td><td>2.40-5.25</td><td>0.78</td></tr><tr><td>log(γ<sub>f</sub>) (%)</td><td>SG2SNV</td><td>5</td><td>0.76</td><td>0.34</td><td>0.86 [0.71; 0.95]</td><td>0.21 [0.13; 0.28]</td><td>-0.04</td><td>3.19</td><td>1.04-3.84</td><td>0.67</td></tr></tbody></table>

iii bold rows indicate models with the best predictive performance.

a RPD interpretation: 1.5–2 = low/high discrimination; 2–2.5 = quantitative prediction; ≥2.5 = good/excellent performance (see Materials and Methods).

b Values in brackets indicate 95 % confidence intervals obtained by bootstrapping (see Materials and Methods).

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Principal Component Analysis (PCA) performed on physico-chemical and physical quality traits of mango purées with score plots (A) and loading plots (B) for the two first components (PC1 and PC2). a*: red to green transition, b*: blue to yellow transition, DM: dry matter (g. 100 g<sup>-1</sup> purée), D [3;2]: Sauter mean diameter (µm), D [4;3]: De Brouckere mean diameter (µm), G'<sub>LVE</sub>: storage modulus (Pa), G''<sub>LVE</sub>: loss modulus (Pa), k: consistency index (Pa.s<sup>a</sup>), L*: lightness (black to white transition), n: flow behavior index (/), pH (/), TA: titratable acidity (g citric acid/100 g purée), TSS: total soluble solids (°Bx), γf: shear strain at flow point (%).</figcaption></figure>

correlated with the flow behavior index n, and there is no significant correlation of this component with the physico-chemical parameters.

The projection of the 61 mango purées onto component PC 1 of the PCA made it possible to distinguish the purées according to their ripening stage. Indeed, the early ripening stages are characterized by their lighter color and higher titratable acidity. This is more noticeable for the Cogshall and Tommy Atkins varieties. Late-stage purées are characterized by a darker color, higher a*, higher soluble solids, and higher pH. As ripening progresses, the shear strain γf also increases. José variety purées follow the same trend, but have naturally higher TSS and DM, even at a less advanced ripening stage. The PC2 projection makes it possible to discriminate purées according to their varietal origin, on the basis of their rheological (flow and viscoelastic behaviors) and granulometric properties. Indeed, Cogshall variety purées differ from José variety purées, with a higher flow index n, lower elastic and viscous moduli (G'<sub>LVE</sub> and G''<sub>LVE</sub>), and finer particle size (lower D[3;2] and D [4;3]). This projection shows that Cogshall purées have physical properties very different from those of José purées. As regards Tommy Atkins purées, it is more difficult to distinguish them from the other two varieties, given the significant evolution of the values of the parameters studied with ripening stages. Thus, early-stage Tommy Atkins purées are very different from the other two varieties. Yet as the ripening stages progress these parameters evolve, and the purées tend to have properties more similar to those of the Cogshall variety. During ripening, these

parameters evolve, and the purées tend to have properties more similar to those of the Cogshall variety than to those of José, notably with finer granulometries and lower rheological indicators—with the exception of n, which increases with ripening and indicates better flow. This PCA analysis highlights the joint role of variety and ripening in the development of the physico-chemical, rheological, and granulometric properties of mango purées. While similar results can be found in the literature concerning the evolution of physico-chemical and rheological parameters during ripening, notably in Labaky et al. (2021), it is difficult to find references on the effect of variety on these same properties.

Fig. 4 shows the evolution of the purée parameters (D [3;2], G'<sub>LVE</sub>, TSS, γf and L*) from the three varieties as a function of D5N, which reflects the fruit firmness and hence its ripening. These evolutions were mathematically modelled, and the optimal models for representing the relationships between the various physico-chemical and physical parameters of the purées and fruit firmness (D5N) are reported in Table 1. The coefficient of determination (R<sup>2</sup>) of the models obtained were generally greater than 0.5 (9 times over 15), indicating an acceptable accuracy of data prediction by the model. These values were in accordance with the RMSE values, which were also generally low compared to the measurement range. A similar trend can be observed for two parameters, D [3;2] and G'<sub>LVE</sub>, which decrease as ripening progresses, with a sharp drop in values at first, then slowing down and stabilizing, particularly above a D5N of 2 mm. This shows that although the fruit
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Evolution of physico-chemical, color, particle size and rheological quality traits of mango purées as a function of fruit firmness for three varieties. D5N (mm), D [3;2]: Sauter mean diameter (µm), G'_{LVE}: storage modulus (Pa), L*: lightness (black to white transition), TSS: total soluble solids (°Bx), yf: shear strain at flow point (%).</figcaption></figure>

becomes softer as ripening progresses, the particle size and solid
behavior of the purées remain stable. These observations are consistent
with the results obtained and described by Labaky et al. (2021) for Kent
mango purées. It should be noted that an asymptotic model is selected
for D [3;2], as this model has the lowest AIC and BIC parameters. The
changes in D [3;2] with increasing D5N and the 3 coefficients (α, β, Ω)
estimated for this model were similar regardless of the variety, showing
that the latter does not play a major role in particle size - thus, a model
common to the 3 varieties can be adopted. As described in Labaky et al.
(2021), a decrease in mango purée particle size was observed with
advancing ripening, for Kent variety mangos. This behavior has also
been reported for purées from fruits characterized by advanced ripening
or storage conditions, such as the tomato (Page et al., 2022; Sinkora
et al., 2023) or apple (Lan, 2021). Conversely, for G'_{LVE}, TSS, yf and L*,
at least one of the model coefficients was affected by the variety.

In fact, the α coefficient, which describes the asymptote of the G'_{LVE} model as a function of D5N, was significantly different according to the variety. The José variety has the highest α value, which translates into consistently higher G'_{LVE} values for this variety compared throughout the D5N range, from approximately 1.5 mm–4 mm. The β coefficient, which reflects the evolution of G'_{LVE} values for very low D5N (<0.5 mm), is significantly different for the Tommy Atkins and José varieties, with a higher value for Tommy Atkins, followed by Cogshall and then José. For Tommy Atkins and also Cogshall, the abrupt decrease in G'_{LVE} values for purées from early-ripening fruits reflects a drastic change in the solid viscoelastic behavior of the purée's particle network, particularly during the early stages of ripening, as reported in the literature for the Kent variety.

In contrast, for the José variety, the significantly lower β coefficient value reflects much less noticeable variations in G'_{LVE} for slightly ripe fruit, despite a particle size that evolves significantly in this early ripening range, as described above. This unique behavior of the José variety, with a more significant impact of ripening on purée particle size than on their solid character, could be explained by the physico-chemical specificity of this variety. The solid-like behavior of plant-based suspensions depends not only on particle size, but also on their microstructure and the strength of particle-particle interactions (Demoulin et al., 2022). In the case of José, it could be suggested that smaller-sized particles encountered in purées from advanced ripening stages, have greater particle-particle interaction, leading to a

microstructure with greater overall solid-like behavior despite their
smaller individual size. Hence, a decrease in particle size can be
accompanied by increased interactions between these smaller physical
entities, resulting in a more structured medium with a more solid
characters observed in the case of the dispersed phase of orange juice
(Demoulin et al., 2022). Similar trends were observed for orange juice
after its fractionation by shearing where smaller particles had a higher
cohesion energy, indicating stronger particle-particle interaction for
colloidal and supra-colloidal matter (Dahdouh et al., 2018). Moreover,
the high TSS content and cell walls composition (namely pectin) of José
variety mangos could favor chemical or physical cross-linking between
pectin in ground purées—thereby offsetting the particle size decrease,
and hence leading to stable solid behavior during ripening. For TSS
changes, the same optimal model was obtained, however only the α
value was significantly affected by the variety. The highest α coefficient
value for José confirmed the higher TSS values in ripe fruits for this
variety than for Cogshall, as reported in literature (Rosalie et al., 2013).
No differences between varieties were noted for the β and Ω coefficients,
showing the same evolution of TSS with decreasing firmness (increasing
D5N). For L* and yf, other optimal models were selected. A Michaelis
Menten model was obtained to accurately describe L* changes as a
function of D5N. The α and β coefficients of the equation are signifi-
cantly impacted by variety, especially for Tommy Atkins, whose pa-
rameters differ from those of the other two varieties. However, these
differences should be considered with caution in view of the low coef-
ficient of determination (R²) value, and the higher RMSE than for the
other two varieties. On the basis of this analysis, it can be suggested that
the evolution of L* with ripening follows a trend which is common to the
3 varieties, with a significant decrease for D5N values below 1.5 mm,
then a stabilization even for high D5N values.

For yf the optimal model was logarithmic, with a significant effect of
variety only on the β coefficient. For Tommy Atkins, yf varies only
slightly with ripening, resulting in a low β coefficient and a low coeffi-
cient of determination (R²). This observation underlines the minor role
of ripening on the transition from a predominantly solid viscoelastic
behavior to a predominantly liquid behavior for Tommy Atkins purées.
In contrast, Cogshall and José purées have yf values that increase
significantly with ripening. This is in phase with the β coefficients, which
are higher for both varieties, though Cogshall has a higher value. Thus,
for the Cogshall variety, higher yf values imply higher deformations
required to switch from a viscoelastic solid-like to a viscous-like behavior. This can be explained by the biochemical and physico-chemical modifications encountered during ripening of this mango variety, which simultaneously affect the properties of the dispersed phase and the dispersing phase of the purées. For example, the degradation of insoluble pectins, which become more soluble and therefore more reactive and prone to gel formation, notably with increasing soluble sugars (higher TSS values) due to starch degradation. These modifications in turn lead to new biochemical and physical reactivity in the medium, resulting in a modification of the overall viscoelastic behavior of the purée, and in particular its yf.

To sum up, this modeling approach to describe the evolution of the purée physico-chemical and physical parameters as a function of the firmness of the fruit from which it is derived has highlighted the fact that both ripening and variety play a joint role in the evolution of these parameters. However, the varietal effect is more or less marked depending on the parameter in question. For example, for D [3;2], ripening is the only factor modulating the particle size indicator. yf, on the other hand, is more variety-dependent, although for some varieties, ripening can further modulate this parameter. It is also interesting to note that G'LVE for the José variety seems to be little affected by ripening, unlike the other two varieties. This observation underlines the complexity of the physical behavior of mango purées, and the importance of jointly considering varietal origin and ripening conditions to achieve the targeted purée quality. So, depending on the targeted purée characteristics, by modulating ripening, only the José and Cogshall varieties could achieve similar yf properties—while all 3 varieties could achieve similar particle sizes. However, for color and TSS, these two parameters remain governed by the variety, and the role of ripening remains minor—particularly in D5N ranges above 1.5 mm, which generally correspond to the ripening stage used for consumption or purée processing.

## 3.2. Chemometric analysis

The absorption spectra of the fruits used to produce the different purées exhibit similar spectral profiles, characterized by the same main absorption bands (Fig. 5). The variations in absorbance values reflect the diversity of the dataset, which includes three mango varieties at different ripening stages. No atypical or aberrant spectra were detected. In the visible range, the absorption bands are observed at 430 nm (blue) and 670 nm (red), corresponding to conjugated double-bond systems, such as carotenoids and chlorophyll (Nordey et al., 2014, 2017). Although wavelengths below 500 nm were not used in this work due to high noise, signals from 500 nm onwards remain sufficiently informative about the pigments. In the near-infrared region, strong absorption bands associated with O-H bond overtones from water are observed at 990 nm, 1450 nm, and 1930 nm (Osborne et al., 1993). The high variability in absorption for color and water-related bands reflects the

heterogeneity of the dataset in terms of ripening stages and varietal differences. Additional absorption bands were detected at 1768 nm, corresponding to C-H stretching vibrations of methylene (-CH₂) groups, characteristic of lipids (fatty acids, triglycerides), and vegetable oils. The band at 2100 nm can be attributed to combined C-H and O-H bond vibrations in complex carbohydrates (starch, cellulose) (Osborne et al., 1993; Workman & Weyer, 2007). As stated, above, different data pre-processing methods (first and second derivative, SNV, etc.) were used to remove or reduce the undesired artefacts from the spectra to increase the predictive accuracy of PLSR (Engel et al., 2013). The models with the fewest prediction errors were selected for the calibration and validation datasets. Models with similar accuracy but fewer factors were selected to increase the robustness of the results. These predictive trends are visually illustrated in Fig. 6 to compare the PLS-predicted values with the laboratory measured purée quality traits for TSS, D[3;2], G'LVE and yf. As shown in Table 2, the models developed for DM and TSS achieved satisfactory predictive performance, with validation R² values of 0.94 and 0.75, respectively. The corresponding RPD values (4.17 for DM and 2.30 for TSS) indicate, according to commonly accepted thresholds, excellent and satisfactory predictive abilities (Nicolaï et al., 2007; Williams, 1987, pp. 143–167). The DM PLS model, based on whole-fruit spectra, showed a particular strong predictive ability, with a RMSEC (0.72 %) close to the RMSEP (0.63 %), and a 95 % confidence interval for the RMSEP (0.45; 1.93 %), estimated via bootstrap resampling. These results suggest that NIRS could reliably estimate purée dry matter content from whole-fruit spectra. The PLSR model for TSS also demonstrated promising performance (R²val = 0.75; RMSEP = 1.32 °Brix; RPD = 2.30), supporting its potential for quantitative prediction within the studied varieties and ripening stages. In contrast, the models for pH and TA showed a worse predictive performance, with RPD values below 1.5 for pH and around 1.70 for TA. These results indicate limited predictive relevance, an observation consistent with previous reports highlighting the difficulty of modelling these parameters (Jha et al., 2012; Nordey et al., 2014, 2017). The models for colour coordinates (L*, a*, b*) showed a moderate predictive performance (RPD ≈ 1.5–2.0), suggesting their possible use for coarse screening of purée color attributes. The purée particle size was well characterized by the model developed for D[3;2], with an R²V of 0.78, an RPD of 2.48, and an RMSEP of 0.27 µm, indicating quantitative predictive ability. However, the modeling of the D[4;3] criterion showed limited performance (R²V = 0.54; RMSEP = 0.21 µm; RPD = 2.23), likely due to the lower variability of this parameter. The viscoelastic behavior of the purées was effectively characterized using models developed for the three criteria (G'LVE, G''LVE, and yf). These models showed similar performance, with R² values ranging from 0.76 to 0.86 in both calibration and validation. The RPD values were 2.66, 2.60, and 3.19, respectively, while the RMSEP values were 0.30 Pa for G'LVE, 0.30 Pa for G''LVE, and 0.21 % for yf. This performance indicated that all three models provided quantitative predictions, with the yf model showing the highest predictive performance. The flow behavior of the purées cannot be reliably evaluated with the PLSR model developed for the consistency coefficient (k). The model showed poor predictive performance (R²V = 0.31; RMSEP = 7.50 Pa·s⁻¹; RPD = 1.45), indicating that it was unable to discriminate between samples of different viscosities. However, the approach failed to develop an efficient model for the flow behavior index (n), likely due to the low variability observed for this parameter.

Taken together, these results support the hypothesis that NIRS can effectively capture key biophysical variations between mango purées, mainly driven by varietal and ripening effects. The consistency between calibration and validation performances, reinforced by repeated cross-validation, provides stronger evidence of model reliability compared with most fruit-based NIRS studies (Chaudhary et al., 2025; Nordey et al., 2017; Theanjumpol et al., 2013). However, as this work represents a proof of concept, its predictive scope remains limited by the sample size, varietal diversity, and the controlled laboratory setting. Broader datasets and on-line applications will be required to confirm the

<figure><img src="image_6.png" /><figcaption>Fig. 5. Vis-NIRS spectra obtained on unpeeled fruits for three varieties.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** PLS-predicted purée quality traits based on Vis-NIRS fruit spectra vs. measured purée quality traits for TSS: total soluble solids (°Bx), D [3;2]: Sauter mean diameter (µm), G'_{LVE}: storage modulus (Pa) and γ_f: shear strain at flow point (%).</figcaption></figure>

transferability and robustness of the models under industrial conditions. From an applied perspective, NIRS appears to be a promising non-destructive approach for evaluating fruit characteristics and predicting the quality attributes of the resulting purées—thereby aiding better control of textural and compositional consistency during processing. For example, processors could use NIRS to non-destructively sort fruits according to predicted purée traits with higher storage modulus values (G'_{LVE}) or specific particle sizes, thus standardizing texture without post-processing.

## 4. Conclusion

The results of this study showed that mango variety and ripening had a joint role in the physio-chemical properties of the purées. A similar effect of ripening was observed on the disparity of physico-chemical characteristics within the same variety, across the 3 studied varieties—with the exception of TA, which seems to vary very little during ripening for the José variety. The principal component analysis (PCA) highlighted those purées from high firmness fruits were characterized by their lighter color and higher titratable acidity, whereas purées from low firmness fruits had higher total soluble solids and pH. It was also possible to distinguish the purées according to their varietal origin, on the basis of their rheological and granulometric properties. This work provided also new knowledge concerning the possibility of using near-infrared spectra of whole fruits to characterize the quality of mango purées from three contrasted mango varieties. In summary, the present study demonstrated a novel, non-destructive approach to predicting key physico-chemical, rheological and granulometric properties of mango purées from whole fruits. Strengths include the simultaneous analysis of three distinct varieties and multiple ripening stages, integration of comprehensive quality traits, rigorous statistical validation, and direct industrial relevance for optimizing raw material sorting. Future studies are needed to extend these findings to other varieties, and production systems to achieve higher representativity and genericity of this knowledge. Moreover, extensive biochemical characterization of purées would provide further knowledge for a better understanding of the joint role of variety and ripening on purée quality traits.

## CRediT authorship contribution statement

**Julien Ricci:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Nathan Tanguy:** Writing – original draft, Visualization, Software, Investigation, Data curation. **Camille Demoulin:** Validation, Methodology, Formal analysis, Conceptualization. **Lidwine Grosmaire:** Validation, Supervision, Methodology, Conceptualization. **Simon Carlier:** Methodology, Data curation. **Doralice Veillant:** Methodology, Investigation. **Fabrice Davrieux:** Writing – original draft, Validation, Software, Methodology, Data curation. **Mathieu Léchaudel:** Visualization, Validation, Software, Investigation, Data curation. **Layal Dahdouh:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

## Funding and acknowledgments

This work was carried out as part of the CIRAD DPP SADur agro-nomical research program. This program is co-funded by the European Union, the French State, and the La Réunion Region. Europe is committed to La Réunion through the ERDF (grant 2024-1248-005756), for which the La Réunion Region is the managing authority.

## 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.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.lwt.2025.118917.

## Data availability

Data will be made available on request.
## References

Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variant transformation and De-Trending of near-infrared diffuse reflectance spectra. *Applied Spectroscopy*, **43**(5), 772-777. https://doi.org/10.1366/0003702894202201

Brandolini-Bunlon, M., Jaillais, B., Roger, J.-M., & Lesnoff, M. (2024). Rchemo: Dimension reduction, regression and discrimination for chemometrics [Computer software] Version 0.1-3. https://cran.r-project.org/web/packages/rchemo/index.html.

Büergy, A. (2021). *Modulation of the texture and tissue fragmentation of fruits during thermal treatments by the methods of cultivation and maturation: Impact on the texture of the purees* (issue 2021AVIG0282) [Theses, Université d'Avignon] https://theses.hal.sciences tel-03211046.

Chaudhary, R. K., Neupane, A., Wang, Z., & Walsh, K. (2025). Mango quality assessment using near-infrared spectroscopy and hyperspectral imaging: A systematic review. *Agronomy*, **15**, 2271. https://doi.org/10.3390/agronomy15102271

Dahdouh, L., Delalonde, M., Ricci, J., Ruiz, E., & Wisniewski, C. (2018). Influence of high shear rate on particles size, rheological behavior and fouling propensity of fruit juices during crossflow microfiltration: Case of orange juice. *Innovative Food Science & Emerging Technologies*, **48**, 304-312. https://doi.org/10.1016/j.ifset.2018.07.006

Dahdouh, L., Wisniewski, C., Ricci, J., Kapitan-Gnimdu, A., Dornier, M., & Delalonde, M. (2015). Development of an original lab-scale filtration strategy for the prediction of microfiltration performance: Application to orange juice clarification. *Separation and Purification Technology*, **156**, 42-50. https://doi.org/10.1016/j.seppur.2015.10.010

Dahdouh, L., Wisniewski, C., Ricci, J., Vachoud, L., Dornier, M., & Delalonde, M. (2016). Rheological study of orange juices for a better knowledge of their suspended solids interactions at low and high concentration. *Journal of Food Engineering*, **174**, 15-20. https://doi.org/10.1016/j.jfoodeng.2015.11.008

Demoulin, C., Dahdouh, L., Ricci, J., Ruiz, E., Delalonde, M., & Wisniewski, C. (2023). Synergistic effect of particle size, shear rate and driving-force during microfiltration of fruit juices: Toward a relevant choice of pretreatments and filtration conditions. *Innovative Food Science & Emerging Technologies*, **84**, Article 103247. https://doi.org/10.1016/j.ifset.2022.103247

Demoulin, C., Wisniewski, C., Ricci, J., Delalonde, M., & Dahdouh, L. (2022). Viscoelastic behavior and fouling propensity of concentrated suspended particles of orange juice with defined size distributions: Towards a better control of the deposit layer properties during microfiltration. *LWT*, **153**, Article 112473. https://doi.org/10.1016/j.lwt.2021.112473

Demoulin, C., Wisniewski, C., Ricci, J., Kapitan-Gnimdu, A., Delalonde, M., & Dahdouh, L. (2023). Innovative approach to predict the fouling propensity of orange juice suspended particles through relevant physical characterisation. *International Journal of Food Science and Technology*, **58**(3), 1049-1061. https://doi.org/10.1111/ijfs.16242

Djioua, T. (2010). *Amélioration de la conservation des mangues 4ème gamme par application de traitements thermiques et utilisation d'une conservation sous atmosphère modifiée* [Phdthesis, Université d'Avignon]. https://theses.hal.science/tel-00544995.

Ellong, E. N., Adenet, S., & Rochefort, K. (2015). Physicochemical, nutritional, organoleptic characteristics and food applications of four mango (Mangifera indica) varieties. *Food and Nutrition Sciences*, **6**(2), 242-253. https://doi.org/10.4236/fns.2015.62025

Engel, J., Gerretzen, J., Szymańska, E., Jansen, J. J., Downey, G., Blanchet, L., & Buydens, L. M. C. (2013). Breaking with trends in pre-processing? *TrAC: Trends in Analytical Chemistry*, **50**, 96-106. https://doi.org/10.1016/j.trac.2013.04.015

Espinosa-Muñoz, L., Renard, C. M. G. C., Symoneaux, R., Biau, N., & Cuvelier, G. (2013). Structural parameters that determine the rheological properties of apple puree. *Journal of Food Engineering*, **119**(3), 619-626. https://doi.org/10.1016/j.jfoodeng.2013.06.014

Evans, E. A., Ballen, F. H., & Siddiq, M. (2017). Mango production, global trade, consumption trends, and postharvest processing and nutrition. In *Handbook of mango fruit* (pp. 1-16). John Wiley & Sons Ltd. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119014362.ch1

Gundurao, A., Ramaswamy, H. S., & Ahmed, J. (2011). Effect of soluble solids concentration and temperature on thermo-physical and rheological properties of mango puree. *International Journal of Food Properties*, **14**(5), 1018-1036. https://doi.org/10.1080/10942910903580876

He, H.-J., Wu, D., & Sun, D.-W. (2015). Nondestructive spectroscopic and imaging techniques for quality evaluation and assessment of fish and fish products. *Critical Reviews in Food Science and Nutrition*, **55**(6), 864-886. https://doi.org/10.1080/10408398.2012.746638

Jha, S. N., Jaiswal, P., Narsaiah, K., Gupta, M., Bhardwaj, R., & Singh, A. K. (2012). Non-destructive prediction of sweetness of intact mango using near infrared spectroscopy. *Scientia Horticulturae*, **138**, 171-175. https://doi.org/10.1016/j.scienta.2012.02.031

Jiang, T., Ding, J., Du, Y., Yuan, S., Yu, H., & Yao, W. (2025). Deep learning-driven Vis/NIR spectroscopic devices for fruit quality assessment: A comprehensive review. *Trends in Food Science & Technology*, **164**, Article 105262. https://doi.org/10.1016/j.tifs.2025.105262

Kaboorani, A., & Blanchet, P. (2014). Determining the linear viscoelastic region of sugar maple wood by dynamic mechanical analysis. *Bioresources*, **9**(3), 4392-4409. https://doi.org/10.15376/biores.9.3.4392-4409

Kennard, R. W., & Stone, L. A. (1969). Computer aided design of experiments. *Technometrics*, **11**(1), 137-148. https://doi.org/10.2307/1266770

Labaky, P., Dahdouh, L., Ricci, J., Wisniewski, C., Pallet, D., Louka, N., & Grosmaire, L. (2021). Impact of ripening on the physical properties of mango purees and application of simultaneous rheometry and in situ FTIR spectroscopy for rapid identification of biochemical and rheological changes. *Journal of Food Engineering*, **300**, Article 110507. https://doi.org/10.1016/j.jfoodeng.2021.110507

Labaky, P., Grosmaire, L., Ricci, J., Wisniewski, C., Louka, N., & Dahdouh, L. (2020). Innovative non-destructive sorting technique for juicy stone fruits: Textural properties of fresh mangos and purees. *Food and Bioproducts Processing*. https://doi.org/10.1016/j.fbp.2020.06.013

Lan, W. (2021). *Applications using infrared spectroscopy to detect and bridge the variability and heterogeneity before and after fruit processing: A case study on apple purees* (issue 2021AVIG0286) [Theses, Université d'Avignon] https://theses.hal.science/tel-04045700.

Lan, W., Jaillais, B., Leca, A., Renard, C. M. G. C., & Bureau, S. (2020). A new application of NIR spectroscopy to describe and predict purees quality from the non-destructive apple measurements. *Food Chemistry*, **310**, Article 125944. https://doi.org/10.1016/j.jfoodchem.2019.125944

Nawar, S., & Mouazen, A. M. (2018). Optimal sample selection for measurement of soil organic carbon using on-line vis-NIR spectroscopy. *Computers and Electronics in Agriculture*, **151**, 469-477. https://doi.org/10.1016/j.compag.2018.06.042

Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. *Postharvest Biology and Technology*, **46**(2), 99-118. https://doi.org/10.1016/j.postharbio.2007.06.024

Nordey, T., Joas, J., Davrieux, F., Chillet, M., & Léchaudel, M. (2017). Robust NIRS models for non-destructive prediction of mango internal quality. *Scientia Horticulturae*, **216**, 51-57. https://doi.org/10.1016/j.scienta.2016.12.023

Nordey, T., Joas, J., Davrieux, F., Génard, M., & Léchaudel, M. (2014). Non-destructive prediction of color and pigment contents in mango peel. *Scientia Horticulturae*, **171**, 37-44. https://doi.org/10.1016/j.scienta.2014.01.025

Nur Azira, T., Che Man, Y. B., Raja Mohd Hafidz, R. N., Aina, M. A., & Amin, I. (2014). Use of principal component analysis for differentiation of gelatin sources based on polypeptide molecular weights. *Food Chemistry*, **151**, 286-292. https://doi.org/10.1016/j.jfoodchem.2013.11.066

Osborne, B. G., Fearn, T., & Hindle, P. T. (1993). *Practical NIR spectroscopy with applications in food and beverage analysis* (2nd ed.). Wiley: Longman Scientific & Technical.

Page, D., Villas-Boas, A., Fanciullino, A.-L., Sinkora, M., Giovinazzo, R., Zuber, F., & Bertin, N. (2022). Impact of a low water input, genotype and process on the texture of purees from two tomato cultivars. *Acta Horticulturae*, **1351**, 19-24. https://doi.org/10.17660/ActaHortic.2022.1351.4

Parvez, G. M. (2016). Pharmacological activities of mango (mangifera indica): A review. *Journal of Pharmacognosy and Phytochemistry*, **5**(3), 1-7.

Phaokuntha, S., Poonlarp, P. B., & Pongsirikul, I. (2014). Rheological properties of mango puree and process development of mango sheet. *Acta Horticulturae*, **1024**, 373-379. https://doi.org/10.17660/ActaHortic.2014.1024.51

Pronprasit, R., & Natwichai, J. (2013). Prediction of mango fruit quality from NIR spectroscopy using an ensemble classification. *International Journal of Computer Applications*, **83**(14), 25-30. https://doi.org/10.5120/14517-2903

Rivier, M., Méot, J.-M., Ferré, T., & Briard, M. (2009). Le séchage des mangues. *Éditions quae*. https://www.quae-open.com/product/11/9782759203420/le-sechage-des-mangues

Rosalie, R., Chillet, M., Joas, J., Lechaudel, M., Payet, B., Vulcain, E., & Dufossé, L. (2013). Evaluation of quality parameters and carotenoid content of three cultivars of mango (Mangifera indica L.) from Réunion island, 42 https://hal.univ-reunion.fr/hal-01187341.

Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. *Analytical Chemistry*, **36**(8), 1627-1639. https://doi.org/10.1021/ac60214a047

Sinkora, M., Fanciullino, A.-L., Page, D., Giovinazzo, R., Lanoë, L., Boas, A. V., & Bertin, N. (2023). Tomato puree quality from field to can: Effects of water and nitrogen-saving strategies. *European Journal of Agronomy*, **149**, Article 12689. https://doi.org/10.1016/j.eja.2023.126891

Strani, L., Grassi, S., Alamprese, C., Casiraghi, E., Ghiglietti, R., Locci, F., Pricca, N., & De Juan, A. (2021). Effect of physicochemical factors and use of milk powder on milk rennet-coagulation: Process understanding by near infrared spectroscopy and chemometrics. *Food Control*, **119**, Article 107494. https://doi.org/10.1016/j.jfoodcont.2020.107494

Theanjumpol, P., Self, G., Rittiron, R., Pankasemsu, T., & Sardsud, V. (2013). Selecting variables for near infrared spectroscopy (NIRS) evaluation of mango fruit quality. *Journal of Agricultural Science*, **5**(7), Article p146. https://doi.org/10.5539/jas.v5n7p146

Tiyayon, C., & Paull, R. E. (2017). Mango production. In *Handbook of mango fruit* (pp. 17-35). John Wiley & Sons Ltd. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119014362.ch2

Valente, M., Dornier, M., Piombo, G., & Grotte, M. (2004). Mango fruit firmness and pulp starch content. *Fruits*, **59**(6), 399-410. https://doi.org/10.1051/fruits:2005002

Walsh, K. B., McGlone, V. A., & Han, D. H. (2020). The uses of near infra-red spectroscopy in postharvest decision support: A review. *Postharvest Biology and Technology*, **163**, Article 111139. https://doi.org/10.1016/j.postharvio.2020.111139

Williams, P. (1987). Variables affecting near-infrared reflectance spectroscopic analysis. *Near-infrared technology in the agricultural and food industries*.

Workman, J. J., & Weyer, L. (2007). *Practical guide to interpretive near-infrared spectroscopy*. CRC Press. https://doi.org/10.1201/9781420018318

Zakaria, A., Shakaff, A. Y. M., Masnan, M. J., Saad, F. S. A., Adom, A. H., Ahmad, M. N., Jaafar, M. N., Abdullah, A. H., & Kamarudin, L. M. (2012). Improved maturity and ripeness classifications of magnifera indica cv. harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor. *Sensors*, **12**(5), 6023-6048. https://doi.org/10.3390/s120506023