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Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality
(2026-07-01)
Sahachairungrueng, Woranitta
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Aozora, Wayan Dipasasri
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Tantinantrakun, Achiraya
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Suwapanich, Rachit
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Workhwa, Saranya
The quality of sweet tamarind fruit, as determined by its total soluble solids (TSS), titratable acidity (TA), and TSS/TA ratio, is important for consumer satisfaction. Nondestructive techniques are therefore required to assess the quality of sweet tamarind fruit. This study investigated whether near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm can be used as a non-destructive method to assess TSS, TA, and the TSS/TA ratio of sweet tamarind fruit and to classify it under commercial standards. NIR-HSI-based chemometric and machine-learning modeling was applied for quantification and qualification analyses. Calibration models for determining TSS, TA, and the TSS/TA ratio were developed using partial least squares regression (PLSR) and support vector machine regression (SVMR). A combination of first derivative and SNV spectral pretreatment was optimized to establish an SVMR model for TSS determination. MSC spectral pretreatment was optimized to develop the SVMR model for TA assessment, and the first derivative spectral pretreatment was optimized to establish an SVMR model for the TSS/TA ratio. Correlation coefficients of prediction (R<inf>p</inf>) of 0.959, 0.961 and 0.957 were obtained with root mean square errors of prediction (RMSEP) of 1.102%, 0.369% and 7.850, and a ratio of performance to deviation (RPD) of 3.29, 3.52 and 3.34 for the TSS, TA, and TSS/TA ratio evaluations, respectively. Partial least squares–discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used for classifying sweet tamarind fruit under a commercial acidity standard (≤4%). The SVMC with SNV spectral pretreatment produced the best prediction results for distinguishing standard and off-standard sweet tamarind fruit with an 82.86% accuracy. NIR HSI can be used to non-destructively predict the quality of tamarind fruit. It can be applied for online sorting to evaluate individual sweet tamarind fruits for grading and quality control in factory environments.
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Rapid detection of potassium sorbate in coconut water using near infrared hyperspectral imaging
(2026-01-01)
Tantinantrakun, Achiraya
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Kumpa, Benjaporn
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Ainkast, Pranpriya
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Thompson, Anthony Keith
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Teerachaichayut, Sontisuk
Potassium sorbate may be illegally added to fresh coconut water in order to prolong its marketable life, but this adulteration may not be identified on the product label. The aim of this research was therefore to evaluate if samples of fresh coconut water that had been adulterated with measured amounts of potassium sorbate could be detected by near infrared hyperspectral imaging (NIR-HSI). Samples of coconut water with different potassium sorbate concentrations (N = 100) and pure coconut water samples (N = 100) were used in this study with their averaged spectral data used as independent variables. The smoothing spectral pretreatment gave the highest classification accuracy of 98.48% by partial least squares discriminant analysis (PLS-DA). While support vector machine regression (SVMR) with spectral pretreatment, using the 1st derivative combined with multiplicative scatter correction (MSC), achieved the optimum condition for developing the calibration model for determining potassium sorbate concentration with the correlation coefficient of prediction (R<inf>p</inf>) of 0.818 and the root mean square error of prediction (RMSEP) of 327.86 ppm. The results showed that NIR-HSI was able to be used as a fast, reliable, economic and environmentally friendly method of detecting potassium sorbate addition to coconut water.
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Near-infrared hyperspectral imaging for predicting the quality of SO2 pre-treated and dehydrated mango
(2025-08-01)
Aozora, Wayan Dipasasri
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Tantinantrakun, Achiraya
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Thompson, Anthony Keith
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Teerachaichayut, Sontisuk
Prediction for quality indices of SO<inf>2</inf> pre-treated and dehydrated mango was accessed by NIR-HSI. Models for predicting TSS and SO<inf>2</inf> content achieved R = 0.82; RMSEP = 2.42% and R = 0.83; RMSEP = 56.40 mg/kg, respectively. Visualization of TSS and SO<inf>2</inf> content could be presented by predictive images.
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Coating minimally processed Golden Nam Dok Mai mango with Aloe vera gel extract to maintain quality during storage
(2025-01-01)
Suwapanich, Rachit
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Thompson, Anthony Keith
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Nukthamna, Pikunthong
This study investigated the effects of varying concentrations of Aloe vera gel extract on the post-harvest quality and shelf life of minimally processed 'Golden Nam Dok Mai' mango chunks stored at 5 °C for 7 days. Although all mango chunks softened during storage, slower softening was observed with higher Aloe vera concentrations, except for those treated with 100% A. vera, which showed similar softening to the controls. Coating with A. vera gel reduced weight loss compared to untreated controls, although the concentration of A. vera did not significantly influence weight loss. Total soluble solids increased across all treatments, but 40% A. vera treatment led to the least increase, suggesting moderate metabolic activity. Titratable acidity decreased during storage, with higher A. vera concentrations (40% and 60%) preserving acidity better. Vitamin C content was better retained in mango chunks coated with 100% A. vera. Additionally, A. vera coatings led to a darker flesh over time, particularly with thicker coatings. Sensory evaluation revealed that mango chunks coated with 40% A. vera gel were most preferred. We concluded that coating minimally processed ‘Golden Nam Dok Mai’ mango chunks with A. vera gel would be beneficial in terms of a cost-effective natural preservative to enhance the shelf life and sensory qualities of fresh-cut mangoes.
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Assessing adulterated pineapple juice concentrate using electrical properties
(2025-01-01)
Tantinantrakun, Achiraya
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Sinsamut, Varisara
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Apairat, Nuengruthai
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Smutrakalin, Thirapol
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Thompson, Anthony Keith
The fraudulent addition of sugars to pineapple juice concentrate undermines consumer trust and satisfaction. Resistance (R), capacitance (C), dissipation factor (D), inductance (L), quality factor (Q), impedance (Z) and phase angle (θ) in the range of 0.012–200 kHz of juice adulterated with sugar increasing levels from 0 to 95% at 0.5% (w/w) intervals were tested to determine whether they could be used for detecting adulteration in pineapple juice concentrate using a LCR (inductance, capacitance, resistance) meter. A multiple linear regression (MLR) model was developed for predicting the concentration of additive sugars in samples. Linear discriminant analysis (LDA) was used for classifying pure pineapple juice concentrate and pineapple juice concentrate adulterated with added sugars. The most accuracy in the MLR model was obtained from θ, which achieved a correlation coefficient of prediction (R<inf>p</inf>) of 0.977 and a root mean square error of prediction (RMSEP) of 5.88% w/w. From the LDA analysis, the most accurate parameter for classification was C, which yielded a predictive classification accuracy of 94.57%. Therefore, this technique indicates its potential for use in the fruit juice industry a simple method for routinely testing in order to ensure the non-contamination of products offered for sale to consumers.
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Evaluation of the efficiency of various folic acid microencapsulation techniques for rice vermicelli (Khanom Jeen) fortification
(2024-05-01)
Yingleardrattanakul, Phatthira
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Thompson, Anthony Keith
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Taprap, Ruchira
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Pinsirodom, Praphan
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Bindu, Charan
This study evaluated the efficiency of various folic acid microencapsulation techniques, such as gel particle (GP), coacervation (CC), and spray drying (SD), for fortification of rice vermicelli. Of the microcapsules, the GP capsules were rough and 13 % soluble in acid solutions; the CC capsules formed spontaneously and were water-soluble for 120 min; and the SD capsules had spherical shape and the fastest acid solubility. The different microcapsule types had different levels of folic acid loss. CC capsules dissolved in water within 120 min, whereas GP and SD capsules were water-insoluble. SD capsules had the lowest moisture content, water activity, pH, and total acidity. For rice vermicelli fortification 300 µg of SD encapsulate per 100 g of flour. The analysis of fortified rice vermicelli included water content, folic acid, pH, total acid, sensory evaluation by drying, high performance liquid chromatography (HPLC), pH meter, titration respectively, and sensory testing. In the mixing, extrusion, and first and second washing steps, the wet weight basis of 100 g folic acid SD was 342.02, 470.67, 530.07, and 546.25 µg, the acidity was 3.96, 4.09, 4.12, and 4.17, respectively; the total acid was 0.11 %, 0.07 %, 0.07 %, and 0.07 %, respectively; and the loss of folic acid during the processing in each step was 38 %, 55 %, and 60 %. The sensory test results among groups were not significantly different (p > 0.05). In conclusion, SD was the most suitable technique for folic acid microencapsulation in rice vermicelli because SD microcapsules were acid-soluble but water-insoluble, thus preventing the loss of folic acid during processing.
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Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging
(2024-04-01)
Workhwa, Saranya
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Khanthong, Thitirat
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Manmak, Napatsorn
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Thompson, Anthony Keith
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Teerachaichayut, Sontisuk
Mangosteens can develop a postharvest physiological disorder, called “hardening”, which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value using the texture analyzer. Near-infrared hyperspectral imaging (NIR-HSI) in the region of 935–1720 nm was tested as a possible rapid and non-destructive method to detect this disorder. The spectra from a region of interest of mangosteens were acquired and used for analysis. Calibration models for firmness of a similarly sized group and a mixed-size group were established using partial least squares regression (PLSR) and support vector machine regression (SVMR). Chemometric algorithms were investigated in order to determine the optimal conditions for establishing the models for firmness. The optimum model was obtained when the fruit were graded into similarly sized groups. Using partial least squares regression (PLSR), the correlation coefficient of prediction (R<inf>p</inf>) was 0.87 and the root mean square error of prediction (RMSEP) was 6.25 N. The predictive images for firmness of the fruit were created by interpreting predicted firmness visualized as colors in every pixel. From the data, it was concluded that NIR-HSI can potentially be used to visualize hardening of individual mangosteens based on their predictive images.
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Antidiabetic Property Optimization from Green Leafy Vegetables Using Ultrasound-Assisted Extraction to Improve Cracker Production
(2024-01-01)
Maser, Wahyu Haryati
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Maiyah, Nur
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Karnjanapratum, Supatra
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Nukthamna, Pikunthong
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Thompson, Anthony Keith
Here we test a method of incorporating of plant extracts into popular snack foods to help control diabetes. Since some fresh vegetables contain antidiabetic compounds, ultrasound-assisted extraction was used to optimize their extraction of from spring onions, bunching onions, and celery for later incorporation into crackers. We compared various concentrations of ethanol used during extraction, after which they were exposed to an ultrasound processor whose amplitude and sonication time were also varied. The optimal extraction conditions were found to be an ethanol concentration of 44.08%, an amplitude of 80%, and a sonication time of 30 min. This resulted in the highest level of α-glucosidase inhibitory activity (i.e., 1,449.73 mmol ACE/g) and the highest extraction yield (i.e., 24.16%). The extract produced from these optimum conditions was then used as a constituent component of crackers at 0.625%, 1.25%, or 2.5% w/w. These biscuits were then produced at baking temperatures of 140°C, 150°C, or 160°C. We then measured the physical characteristics and bioactivities of sample biscuits from each treatment. We found that biscuits containing 2.5% vegetable combination extract and baked at 140°C had the highest total phenolic content, the strongest antioxidant performance, and showed the most substantial antidiabetic and antiobesity effects. Here we establish conditions for the effective extraction of antidiabetic functional ingredients via ultrasound from green leafy vegetables. We also provide a method of using these ingredients to prepare crackers with the aim of developing a functional antidiabetic snack food.
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Transient heat modeling for non-destructive assessment of boiled eggs
(2023-10-01)
Sahachairungrueng, Woranitta
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Tonpho, Pasika
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Veeradechakul, Mungkarej
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Rosnim, Thitirat
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Thompson, Anthony Keith
It is not possible to differentiate between hard-boiled and soft-boiled eggs after processing. Therefore, transient heat of boiled eggs was tested during the production process in order to develop a model that could be used to differentiate between soft-boiled and hard-boiled eggs. Both types of boiled eggs (N=214) were produced in water at 90 °C for different times and then cooled down. The temperature gradients due to heat transfer during cooling in the ambient air were measured every 30 seconds. Results showed that a dimensionless parameter, called number of transfer units (NTU), changed in relation to time during the cooling process, and it was shown that this could be used as an independent variable. Classification models were established using linear discriminant analysis (LDA) and support vector machine classification (SVMC). Samples were divided into a calibration set (N=150) and a prediction set (N=64). The predictive accuracy of the models using LDA and SVMC for classifying eggs into soft-boiled or hard-boiled was 93.8% and 92.2%, respectively. Therefore, it was concluded that the classification models using LDA and SVMC had potential for use as a non-destructive method for classifying groups of eggs into soft-boiled and hard-boiled that could potentially be used in a commercial situation.
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Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
(2023-07-01)
Sahachairungrueng, Woranitta
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Thompson, Anthony Keith
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Terdwongworakul, Anupun
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Teerachaichayut, Sontisuk
Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares–discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.

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