Teerachaichayut, Sontisuk

Preferred name

Alternative Name

Teerachaichayut, S.

Main Affiliation

School of Food Industry

sontisuk.te@kmitl.ac.th

Now showing 1 - 10 of 24

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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 (R<sup>2</sup><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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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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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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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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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
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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.
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Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality
(2026-07-01)
Sahachairungrueng, Woranitta
;
Aozora, Wayan Dipasasri
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Tantinantrakun, Achiraya
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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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Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging
(2023-07-01)
Tantinantrakun, Achiraya
;
Thompson, Anthony Keith
;
Terdwongworakul, Anupun
;
Teerachaichayut, Sontisuk
Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (R<inf>p</inf>) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.
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Non-destructive quality assessment of hens’ eggs using hyperspectral images
(2017-12-01)
Suktanarak, Sineenart
;
Teerachaichayut, Sontisuk
Freshness of hens’ eggs is important for consumers and the food processing industry and the Haugh unit (HU) is a commonly used index for freshness. Measurement of HU is destructive and also assumes that the sample that is tested accurately reflects the batch of eggs being processed. This research tests the use reflectance near infrared hyperspectral imaging in the wavelength range of 900–1700 nm for nondestructive prediction of eggs freshness and compared these measurements to HU. To achieve this fresh eggs were stored at 25 °C and were measured after storage for 0, 4, 7, 10, 14, 18 and 21 days by hyperspectral imaging technique and compared to HU for each egg. Hyperspectral imaging technique combines between conventional imaging and NIR spectroscopy to achieve spatial and spectral information from eggs. The acquired near infrared hyperspectral imaging data from samples in the calibration set were analyzed in order to develop a calibration model for HU using partial least squares regression (PLSR) and then crossvalidated. The standard normal variate transformation (SNV) spectral pretreatment gave the optimum conditions for establishing the calibration model with a coefficient of determination (R2) of 0.91 and root mean square error of calibration (RMSEC) of 4.58. Distribution maps of HU were generated from the acquired calibration model by interpretation of predicted HU to different colors using image processing algorithms. Displayed colors of acquired image of eggs were different correspond to the freshness of the eggs based on HU. The results show that the near infrared hyperspectral imaging technique can be possible to use for presenting the images of egg related to HU in order to nondestructively evaluate hens’ eggs freshness.
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Nondestructive prediction of internal browning in pineapple using transmittance short wavelength near infrared spectroscopy
(2013-01-01)
Sukwanit, S.
;
Teerachaichayut, S.
Pineapple [Ananas comosus (L.) Merr.] is one of the most important commercial fruit of Thailand. The taste and consistency of the fruit is of great importance, however "internal browning", a common physiological disorder affecting the fruit, which cannot be identified by visual inspection, makes the product unacceptable for export. In this study, Near Infrared (NIR) spectroscopy in the range of 665-955 nm was investigated as a non-destructive means to identify internal browning. Partial least squares-discriminant analysis (PLS-DA) was used in conjunction with the pre-treated NIR spectra as a first step in the development of an automated method of pineapple fruit sorting. A set of 243 samples was used for this research (131 commercially acceptable pineapples and 112 pineapples suffering from internal browning). A sample of 145 fruits was used for a training set and 98 samples for a test set. The smoothing and the first derivative pretreatment of averaged spectra were performed to obtain the best calibration model. The overall classification accuracy of the PLS-DA/NIR model on the prediction set was 90.8% (47 out of 53 for the sound pineapples and 42 out of 45 for the internally browned pineapples). This study demonstrates that NIR transmittance spectroscopy is potentially a useful nondestructive method that can be used to predict internal browning disorder in intact pineapples. © ISHS 2013.

Teerachaichayut, Sontisuk

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