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Author: search.filters.author.Sahachairungrueng, Woranitta

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    Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality
    (2026-07-01)
    Sahachairungrueng, Woranitta
    ;
    Aozora, Wayan Dipasasri
    ;
    Tantinantrakun, Achiraya
    ;
    Suwapanich, Rachit
    ;
    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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    Transient heat modeling for non-destructive assessment of boiled eggs
    (2023-10-01)
    Sahachairungrueng, Woranitta
    ;
    Tonpho, Pasika
    ;
    Veeradechakul, Mungkarej
    ;
    Rosnim, Thitirat
    ;
    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
    ;
    Thompson, Anthony Keith
    ;
    Terdwongworakul, Anupun
    ;
    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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    Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy
    (2022-10-01)
    Sahachairungrueng, Woranitta
    ;
    Meechan, Chanyanuch
    ;
    Veerachat, Nutchaya
    ;
    Thompson, Anthony Keith
    ;
    Teerachaichayut, Sontisuk
    It has been reported that some brands of roasted ground coffee, whose ingredients are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. Thus, the objective of this research was to test whether near-infrared spectroscopy hyperspectral imaging (NIR-HSI) or Fourier transform infrared spectroscopy (FTIRs) could be used to test whether samples of coffee were pure Arabica or whether they contained Robusta, and if so, what were the levels of Robusta they contained. Qualitative models of both the NIR-HSI and FTIRs techniques were established with support vector machine classification (SVMC). Results showed that the highest levels of accuracy in the prediction set were 98.04 and 97.06%, respectively. Quantitative models of both techniques for predicting the concentration of Robusta in the samples of Arabica with Robusta were established using support vector machine regression (SVMR), which gave the highest levels of accuracy in the prediction set with a coefficient of determination for prediction (R<inf>p</inf><sup>2</sup>) of 0.964 and 0.956 and root mean square error of prediction (RMSEP) of 5.47 and 6.07%, respectively. It was therefore concluded that the results showed that both techniques (NIR-HSI and FTIRs) have the potential for use in the inspection of roasted ground coffee to classify and determine the respective levels of Arabica and Robusta within the mixture.
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    Nondestructive quality assessment of longans using near infrared hyperspectral imaging
    (2022-03-30)
    Sahachairungrueng, Woranitta
    ;
    Teerachaichayut, Sontisuk
    Near infrared hyperspectral imaging (NIR-HSI) is a method that can be used to evaluate the quality of fruit nondestructively. The objective of this research was to study the feasibility of NIR-HSI reflectance mode, within the wavelength of 935-1720 nm, for predicting the quality of longans. The two important factors chosen were: total soluble solids (TSS) and moisture content (MC). Each longan was assessed by first measuring its spectral data then measuring its TSS and MC to establish calibration models using multiple linear regression (MLR) compared with partial least squares regression (PLSR). Original spectra of longans gave the optimum results by PLSR for developing the models with correlation coefficients (Rp) of 0.76 for TSS and 0.88 for MC as well as root mean square error of predictions (RMSEP) of 0.42% and 0.45% respectively. By image processing, the predictive images from the models for TSS and MC were created based on color scales. They showed different colors of longans related to the level of TSS and MC and the deviation in levels in different parts of each longan by the predictive image. The results showed it could be used for grading fruit giving NIR-HSI potential to be developed in on-line systems.