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Postharvest detection of anthracnose (Colletotrichum asianum) on mango fruit (Mangifera indica L. cv Namdokmai Sithong) using near-infrared response
(2026-12-01)
Junto, Apiwat
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Phanomsophon, Thitima
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Sharma, Sneha
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Kaewsorn, Kannapot
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Jongyingcharoen, Jiraporn Sripinyowanich
Anthracnose disease, caused by fungi of the genus Colletotrichum, poses a major threat to mango production and export industries, with Colletotrichum asianum being among the most significant pathogenic species. This work proposes the hypothesis that the simple difference in absorption between anthracnose-infected and noninfected mangoes illustrated by the average near-infrared (NIR) spectra obtained from hyperspectral images could be used for simple differentiation of the two groups. The method of depositing fungal spores by spraying the spores over the fruit surface, not a small area or specific point, allows for the number of spores per unit area to be harmonized and to detect infected or noninfected spores on every pixel of the mango surface using a hyperspectral imaging camera. Important wavelengths for differentiation included water bands of 970, 1190, and 1200 nm which resulted in the greatest difference in absorbance, and bands of chitin, the major component of the fungal cell wall; 1195 nm was the most important band. In addition, the vibration bands of 868 (protein in the fungal cell wall), 1134 (sugar and starch of the mango substrate), 1320 (NIR absorbers in the fungus-sprayed and mango substrate, not specifically defined) and 1069 nm (crystallinity and N-acetyl methyl groups in the fungal chitin and constituents of the mango), differed from each other. These wavelengths can be used for modelling, which can lead to high performance in quantifying the concentration of anthracnose and classifying the strength levels of anthracnose infection. The microbiological mechanism of anthracnose growth on infected mangoes corresponding to changes in the NIR spectrum during the 4 days after spore infection is comprehensively discussed. These results can aid in enhancing early detection and classification techniques for anthracnose-infected mangoes from noninfected mangoes using hyperspectral image sensors.
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Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp
(2026-09-01)
Promnioy, Surasak
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Phetpan, Kittisak
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Riza, Dimas Firmanda Al
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Sharma, Sneha
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Sirisomboon, Panmanas
The development of affordable, real-time quality monitoring tools is essential for industrial applications involving high-value tropical fruits such as durian. This study presents a cost-effective short-wave infrared multispectral imaging (SWIR-MSI) system employing three discrete bandpass filters (880, 905, and 940 nm) integrated with machine learning algorithms for non-destructive evaluation of fresh-cut durian pulp. Compared with conventional point-based NIR spectroscopy and complex hyperspectral imaging systems, the proposed configuration markedly reduces system complexity and cost while maintaining sensitivity to key compositional variations. It accurately predicted dry matter content (DMC) and starch, demonstrating that limited spectral information within the 860–1100 nm range can effectively capture moisture- and carbohydrate-related features. These findings confirm the feasibility of implementing filter-based SWIR imaging as a practical and scalable alternative to hyperspectral systems for on-line fruit quality assessment. Practically, this approach enables rapid, non-destructive, and spatially adaptable evaluation of durian pulp quality, offering significant potential for on-site grading, ripeness classification, and process control in fresh-cut durian production and packaging operations, particularly for small- and medium-scale agro-processors.
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Revealing the Power of Deep Learning in Quality Assessment of Mango and Mangosteen Purée Using NIR Spectral Data
(2025-09-01)
Pornchaloempong, Pimpen
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Sharma, Sneha
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Phanomsophon, Thitima
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Sirisomboon, Panmanas
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Lapcharoensuk, Ravipat
The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation ((Formula presented.)) = 0.914, root mean square error of cross-validation (RMSE<inf>CV</inf>) = 0.688, the ratio of prediction to deviation of cross-validation (RPD<inf>CV</inf>) = 3.367) and TA ((Formula presented.) = 0.762, RMSE<inf>CV</inf> = 0.037, RPD<inf>CV</inf> = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance ((Formula presented.) = 0.702, RMSE<inf>CV</inf> = 0.471, RPD<inf>CV</inf> = 1.666), though the RPD<inf>CV</inf> values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control.
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Effect of Environment Temperature and Relative Humidity on Thermal Emissivity: Study Case of Mango Fruit
(2024-01-01)
Sirisomboon, Panmanas
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Sripinyowanich Jongyingcharoen, Jiraporn
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Junto, Apiwat
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Phanomsophon, Thitima
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Dachoupakan Sirisomboon, Cheewanun
This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained.
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Near-infrared hyperspectral and multispectral imaging principies and applications in the quality of fruits and vegetables
(2023-08-21)
Sharma, Sneha
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Sumesh, K. C.
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Shrestha, Bim Prasad
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Sirisomboon, Panmanas
In recent years, imaging technologies (hyperspectral and multispectral) are being widely investigated and applied as a non-destructive, reliable, and accurate technique to monitor the quality and composition of agricultural products. Over two decades, hyperspectral imaging (HSI) has developed as a promising technology for qualitative and quantitative analysis of fruits and vegetables. The ability to integrate spatial and spectral information in the form of a hypercube is one of the significant advantages of HSI over spectroscopy and other imaging technologies. It enables mapping of the spatial variability or distribution of those Parameters within the sample. Like HSI, multispectral imaging (MSI) is also gaining interest in real-time application in the grading and/or packaging of fruits and vegetables. Hundreds of images over a contiguous wavelength in HSI brings algorithmic processing complexities, whereas fewer wavelengths in multispectral imaging reduces algorithmic complexities and enables faster processing with reliable results. In summary, these imaging technologies are powerful and reliable techniques for the analysis of agricultural products. This chapter will focus on the theory and principles of near infrared HSI and MSI technologies, their components, the mode of image acquisition, and image processing techniques. Finally, the recent application of these imaging techniques for predicting physicochemical properties, antioxidants, chemical components, texture, defects, maturity classification, shape, and size of fruits and agricultural products are presented.
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Near-infrared hyperspectral imaging combined with machine learning for physicochemical-based quality evaluation of durian pulp
(2023-06-01)
Sharma, Sneha
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Sirisomboon, Panmanas
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K.C, Sumesh
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Terdwongworakul, Anupun
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Phetpan, Kittisak
This research reports on the application of near-infrared hyperspectral imaging (NIR-HSI) system for predicting the physicochemical properties; dry matter (DM), total soluble solids (TSS), and fat content (FC) of durian. Partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and 1D convolution neural network (CNN) models: custom, U-Net, and VGG19; were developed to predict DM, TSS, and FC of durian pulp. Feature wavelengths were selected using a genetic algorithm (GA) and successive projection algorithm (SPA). The selected wavelengths were then validated based on the algorithms for regression model development. GA-PLSR model was compelling to predict the DM and FC in durian pulp, which obtained the coefficient of determination for the test set (r2) and root mean square error of prediction (RMSEP) of 0.97 and 1.12% for DM and 0.86 and 0.64% for FC, respectively. The GA-PLSR model provided the best result for the TSS prediction with r2, and RMSEP of 0.90 and 1.40%, respectively, whereas the SPA-PLSR model based on only thirteen wavelengths attained fair result with the r2 and RMSEP of 0.79 and 2.03%, respectively. The above results show that the pushbroom NIR-HSI system achieved promising results for estimating DM, TSS, and FC in durian pulp. This research identified the featured wavelengths that can be used to develop a portable and reliable HSI or multispectral system to be installed at durian packaging firms for quality inspection and grading.
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Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression
(2022-12-01)
Pornchaloempong, Pimpen
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Sharma, Sneha
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Phanomsophon, Thitima
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Srisawat, Kraisuwit
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Inta, Wasan
Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r2) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r2, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.
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Rapid ripening stage classification and dry matter prediction of durian pulp using a pushbroom near infrared hyperspectral imaging system
(2022-02-15)
Sharma, Sneha
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Sumesh, K. C.
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Sirisomboon, Panmanas
This research examined the potential of a pushbroom near infrared hyperspectral imaging (NIR-HSI) system (900–1600 nm) for ripening stage (unripe, ripe, and overripe) classification based on the days after anthesis (DAA) and dry matter (DM) prediction of durian pulp. The performance of five supervised machine learning classifiers was compared including support vector machines (SVM), random forest (RF), linear discriminant analysis (LDA) partial least squares-discriminant analysis (PLS-DA), and k-nearest neighbors (kNN) for the ripening stage classification and a partial least squares regression (PLSR) model was developed for the DM prediction. The classification and regression models were developed and compared using the full and selected wavelengths by genetic algorithms (GA) and principal component analysis (PCA). For classification, LDA showed the best result with a test accuracy of 100% for both full wavelength and selected 135 wavelengths by GA. A total of 11 wavelengths selected from PCA achieved a test accuracy of 93.6% by LDA. The PLSR models predicted the DM with the coefficient of determination of prediction (R<inf>p</inf>2) greater than 0.80 and a root mean square error of prediction (RMSEP) less than 1.6%. The results show that NIR-HSI has the potential to identify ripeness correctly, predict the DM and visualize the spatial distribution of durian pulp. This approach can be implemented in the packaging firms to solve the problems related to uneven ripeness and to inspect the quality of durian based on DM content.
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Integrating Vis-SWNIR spectrometer in a conveyor system for in-line measurement of dry matter content and soluble solids content of durian pulp
(2021-11-01)
Saechua, Wanphut
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Sharma, Sneha
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Nakawajana, Natrapee
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Leepaitoon, Kritsanaphon
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Chunsri, Rashphon
The prediction of dry matter content (DMC) and soluble solids content (SSC) in durian pulp were performed using a small laboratory scale in-line visible and short wave near infrared (Vis-SWNIR) spectroscopic system. The fiber optic diode array spectrometer with a charged coupled device (CCD) detector in a wavelength range of 450−1000 nm was used for spectral data acquisition. The spectra of the sample were acquired on the moving conveyor belt in two different orientations, including scanning in the upright position of pulps collected in 2018 and the stable position by scanning on the side of the pulps collected in 2019. Partial least squares regression (PLSR) was used to establish the relationship between the spectra and observed DMC and SSC values using the different wavelength ranges, including 450−1000, 700−1000, and 800−1000 nm for the comparison. The results showed that the durian pulp should be scanned in the upright position at the center of the pulp. Moving average smoothing preprocessing combined with the standard normal variate (SNV) for DMC and multiple scatter correction (MSC) for SSC gave the best result. The suitable wavelength range for model development to predict the DMC and SSC was 700−1000 nm and 800−1000 nm, respectively. After comparing the results, the optimum model showed the coefficient of determination of calibration (R<inf>C</inf>2), and prediction (R<inf>P</inf>2), root mean square error of prediction (RMSEP), bias, and the ratio of performance to interquartile distance (RPIQ) of 0.88, 0.83, 4.32 %, 1.25 %, and 3.52 for DMC and 0.70, 0.70, 4.0 %, 0.4 %, and 2.2 for SSC prediction.
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Application of a vis-nir spectroscopic technique to measure the total soluble solids content of intact mangoes in motion on a belt conveyor
(2020-01-01)
Sharma, Sneha
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Sirisomboon, Panmanas
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Pornchaloempong, Pimpen
To date, different approaches have been applied to measure the internal parameters of mangoes by destructive and non-destructive techniques. Recently, real-time evaluation of the internal parameters has become important assessment for fruits in high demand. This research aims to develop an online prototype system to measure the total soluble solids (TSS) in mangoes using a fiber optic diode array Visible-Near Infrared (Vis-NIR) spectrometer on a conveyor belt. Spectra were acquired in a wavelength range from 400–1000 nm. The diffuse reflectance spectra of mangoes were subjected to several preprocessing techniques such as moving average smoothing (MAS), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline offset and normalization before model development. The spectral information and corresponding TSS values were used to establish a linear relationship by partial least squares (PLS) regression. Spectra in three wavelength ranges of 400–1000 nm, 600–1000 nm, and 700–1000 nm were used for the model development. Baseline offset combined with MAS showed effective transformation of spectra at a wavelength of 600– 1000 nm. The optimum model was obtained by an external validation technique with a correlation coefficient of calibration set and a prediction set of 0.80 and 0.74, respectively. The root mean square error of the calibration (RMSEC), root mean square error of prediction (RMSEP) and bias were 0.690%, 0.765%, and 0.061%, respectively. The statistical results from PLS regression indicated the feasibility of using the online conveyor system for grading the fruit according to the TSS.

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