Teerachaichayut, Sontisuk

Preferred name

Alternative Name

Teerachaichayut, S.

Main Affiliation

School of Food Industry

sontisuk.te@kmitl.ac.th

Now showing 1 - 10 of 38

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Non-destructive prediction of total soluble solids, titratable acidity and maturity index of limes by near infrared hyperspectral imaging
(2017-11-01)
Teerachaichayut, Sontisuk
;
Ho, Huong Thanh
This study was implemented for non-destructive prediction of total soluble solids (TSS), titratable acidity (TA) and calculation of TSS/TA as a measure of maturity index in intact limes using laboratory-based push-broom hyperspectral imaging (HSI) in reflectance mode in the range of 929–1671 nm. Limes were scanned by the HSI system in order to develop calibration models for predicting TSS, TA and TSS/TA using partial least square regression (PLSR). Original spectra obtained optimal conditions for establishing the models for TSS and TA while smoothing spectra for TSS/TA. The accuracy of the models for TSS, TA and TSS/TA provided coefficient of determination of prediction (R2<inf>p</inf>) of 0.838, 0.694 and 0.775, respectively and root mean square errors of prediction (RMSEP) of 0.237%, 0.288% and 0.049, respectively. Image processing algorithms were then built up by interpreting predictive values, from the models, to colors in each pixel of the images. The predictive visualization of TSS, TA and TSS/TA in all portions of the limes based on a color scale was presented. The results showed that the HSI technique has the capability of predicting TSS, TA and TSS/TA of intact limes non-destructively and the results could be visualized by different colors of the predictive images.
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Qualitative analysis for sweetness classification of longan by near infrared hyperspectral imaging
(2019-10-25)
Sahachairungrueng, W.
;
Teerachaichayut, S.
Near infrared analysis is a nondestructive technique used for determining the quality of various materials including fruit and other food. The objective of this study was to test whether near infrared hyperspectral imaging could be used for classifying sweetness of longan. One hundred and twenty samples were divided into a calibration set (n = 80) and a prediction set (n = 40). The average absorbance spectra from samples in the wavelength range of 935-1720 nm were used in this study. The sweetness of longan was represented by total soluble solids (TSS) which was used to separate fruit into a low sweet (TSS≤ 21.30°Bx) and high sweet fruit (TSS> 21.30°Bx). A classification model was developed in order to classify groups of longan based on sweetness, where 0 = low sweet and 1 = high sweet, by partial least squares discriminant analysis (PLS-DA). Spectra were preprocessed using a Savitzky-Golay smoothing method in order to obtain the optimal performance of the classification model. The results showed an accuracy of the classification model in the calibration set of 85% and the accuracy was 77.5% in the prediction set. Therefore, it was concluded that near infrared hyperspectral imaging has a potential for classifying longans nondestructive based on sweetness.
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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
;
Tantinantrakun, Achiraya
;
Thompson, Anthony Keith
;
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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Detection of internal mold infection in tomato by transmittance near infrared spectroscopy
(2014-10-20)
Jannok, P.
;
Petcharaporn, K.
;
Teerachaichayut, S.
Alternaria alternata is the black mold occurring inside tomato. This defect can be normally found by destructive method but it cannot be detected by visible inspection from outside appearance of intact tomato. Therefore, a non-destructive technique for prediction of internal mold infection in tomato is required. Near infrared (NIR) spectroscopy technique was considered in this research. Transmittance NIR spectra in the range of 665-955 nm of tomato were acquired. Partial least squares-discriminant analysis (PLS-DA) was performed to establish the calibration model. Results indicated that combination of the standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) pretreatment appeared the best method to develop the model. The calibration model was crossvalidated by a training set (N=140) and used for prediction by a test set (N=60). It obtained 85.0% (corrected 88.7% in normal samples and corrected 81.2% in defected samples) and 91.7% (corrected 100% in normal samples and corrected 83.9% in defected samples) of the total accuracy for calibration and prediction, respectively. Moreover, defected samples were classified in 3 levels of infection severity. The accuracies of cross validation for groups of low, medium and high infection severity were investigated and obtained 82.2, 82.4 and 90.0%, respectively. In conclusion, the calibration model from transmittance NIRS technique can be applied for rapid and non-destructive sorting of internal mold infection in intact tomato.
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Development of calibration models to predict texture and total soluble solids in jelly using hyperspectral imaging
(2018-11-09)
Onnom, Poonnada
;
Teerachaichayut, Sontisuk
The quality of foods for the elderly is important which must be controlled in the manufacturing process. A nondestructive technique that is fast, accurate and reliable is required in order to produce the acceptable products for consumers. Hyperspectral imaging technique was used to establish the calibration models for texture and total soluble solids (TSS) of jelly using partial least squares regression. A set of 99 samples were used for calibration and a set of 49 samples were used for prediction. Spectral pretreatments were investigated in order to develop the highest efficiency of the calibration models. The standard normal variate (SNV) spectral pretreatment was selected for development of the model for texture while the smoothing spectral pretreatment was selected for the TSS model. The accuracy of the calibration models for texture obtained the coefficient of determination (R2) of 0.882 and the root mean square error of prediction (RMSEP) of 0.04N and the accuracy of the calibration models for TSS obtained R2 of 0.969 and RMSEP of 1.32°Bx. The results showed that HSI can be used for nondestructive determination of texture and TSS of jelly. It can be applied for an online sorting system for the manufacturing process.
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Non-destructive prediction of hardening pericarp disorder in intact mangosteen by near infrared transmittance spectroscopy
(2011-10-01)
Teerachaichayut, Sontisuk
;
Terdwongworakul, Anupun
;
Thanapase, Warunee
;
Kiji, Kazuaki
A non-destructive technique to predict a hardening pericarp disorder in intact mangosteen is proposed by using near infrared (NIR) transmittance spectroscopy in the wavelength range of 660-960 nm. The study found that the spectral features of normal pericarp mangosteen and hardening pericarp mangosteen were different. The averaged spectra and individual spectra of hardening pericarp mangosteen from a calibration set (N = 560) were used to develop classification models, using partial least squares discriminant analysis (PLS-DA). A model based on individual spectra obtained better classification. The overall accuracy of classification for a prediction set (N = 358) was 91%. Out of 179 samples of normal pericarp fruits, 167 were identified correctly, while 159 samples out of 179 samples with hard pericarp were predicted correctly. The results showed that NIR transmittance spectroscopy can be used to predict hard pericarp disorder in intact mangosteen fruit accurately. © 2011 Elsevier Ltd. All rights reserved.
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Interactance and reflectance near infrared spectroscopy for freshness evaluation of hen eggs
(2018-10-05)
Suktanarak, S.
;
Teerachaichayut, S.
;
Jannok, P.
;
Supprung, P.
Haugh units is an important index for evaluate freshness of hen eggs. High score of Haugh units (≥60) from eggs means those are new fresh eggs. This research is aimed to use near infrared spectroscopy for nondestructive prediction of egg's freshness by quantitative evaluation based on Haugh units. Interactance mode (588-1091 nm) and reflectance mode (1000-2500 nm) of near infrared spectroscopy were investigated in this research. Hen eggs from farm in Thailand were studied by storage at 25°C for 21 days. Samples were taken for measurements at different days of storage (0, 4, 7, 10, 14, 18 and 21 days). A set of 247 samples (165 for calibration and 82 for prediction) was used for interactance mode and a set of 150 samples (102 for calibration and 48 for a prediction) was used for reflectance mode. Calibration models were established and cross-validated using partial least squares regression (PLSR). The accuracies were considered by test in prediction groups. The results showed that the interactance obtained better accuracy for prediction (correlation coefficient, R=0.91 and root mean square error prediction, RMSEP=5.64) when compared with reflectance mode (R=0.83 and RMSEP=7.11). In this study, the interactance near infrared spectroscopy is more suitable to use in application for freshness sorting of hen eggs.
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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>2) 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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Assessing adulterated pineapple juice concentrate using electrical properties
(2025-01-01)
Tantinantrakun, Achiraya
;
Sinsamut, Varisara
;
Apairat, Nuengruthai
;
Smutrakalin, Thirapol
;
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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Determination of translucent content in mangosteen by means of near infrared transmittance
(2012-03-01)
Terdwongworakul, Anupun
;
Nakawajana, Natrapee
;
Teerachaichayut, Sontisuk
;
Janhiran, Athit
Translucent flesh disorder is undesirable in mangosteen meant for export. However, mangosteens are judged as translucent when the translucent flesh is visible on the pulp surface regardless of the quantity of the internal translucent flesh which may result in some mangosteen assessed as normal having the same amount of translucent flesh content as a mangosteen judged as translucent. The critical amount of translucent flesh to be visible on the pulp surface needs to be determined for assessment purposes. A non-destructive technique to measure the translucent content is a practical tool as the first step towards the establishment of the critical value. A non-destructive model was developed to estimate the translucent content in mangosteens using near infrared transmittance. The translucent area of the flesh section on the fruit surface was used to indicate the translucent content. The effects of the orientation of the fruit and also of the light source to the relative position of the detector as well as the effect of the measurement position of the fruit on the predictive performance were examined. The results showed that the best partial least squares model was achieved with spectra acquired from the fruit position which revealed the largest flesh segment (prediction correlation coefficient was 0.86 and root mean square error of prediction was 7.58%). The horizontal stem-calyx fruit axis and a 135° angle from the light source relative to the detector were the optimal fruit orientation and configuration for measurement. © 2011 Elsevier Ltd. All rights reserved.

Teerachaichayut, Sontisuk

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