Teerachaichayut, Sontisuk

Preferred name

Alternative Name

Teerachaichayut, S.

Main Affiliation

School of Food Industry

sontisuk.te@kmitl.ac.th

Now showing 1 - 10 of 11

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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
;
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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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 adulterated pineapple juice concentrate using electrical properties
(2025-01-01)
Tantinantrakun, Achiraya
;
Sinsamut, Varisara
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Apairat, Nuengruthai
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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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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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Rapid detection of potassium sorbate in coconut water using near infrared hyperspectral imaging
(2026-01-01)
Tantinantrakun, Achiraya
;
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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Non-destructive prediction of pH and total soluble solids of lime [Citrus × aurantifolia (Cristm.) Swinge] by visible and near-infrared spectroscopy
(2017-11-25)
Huong, H. T.
;
Teerachaichayut, S.
The non-destructive visible and near-infrared spectroscopy (Vis/NIRS) technique is well suited for evaluating various internal quality indices of fruits quickly and accurately. The objective of this study was to evaluate the relationships between Vis/NIR measurements and the internal quality indices of lime, including pH and total soluble solids (TSS, °Brix). For this experiment, reflectance measurement in the 400-2500 nm range was done on 140 samples for pH and 117 samples for TSS. Partial least square (PLS) regression was used to establish the calibration models. First-order derivative and multiplicative scatter correction spectral pretreatments were used to develop calibration models for pH and TSS, respectively. The correlation coefficient (R) and the root mean square error of prediction (RMSEP) from the calibration model for pH were 0.95 and 0.06. The corresponding values for TSS were 0.81 and 0.24 °Brix, respectively. The results showed that Vis/NIRS measurements in the spectral range 400-2500 nm could be used to access pH and TSS of lime.
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Prediction of water activity in mamón (Filipino sponge) cakes by near infrared hyperspectral imaging
(2020-01-01)
Sricharoonratana, Manunchaya
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Teerachaichayut, Sontisuk
Water activity in foods can result in detrimental microbial activity during storage. The usual methods of water activity measurement involve destruction of the sample. Near infrared (NIR) hyperspectral imaging has previously been successfully used as a non-destructive method to determine various physical and chemical characteristics of a variety of foods. Therefore, this method was tested to determine whether it could be used to measure water activity of mamón cakes, a popular sponge cake developed in the Philippines. Individual samples (n = 178) were divided into a calibration set (n=119) and a prediction set (n=59). These samples were tested using NIR hyperspectral imaging (935-1720 nm) with a smoothing spectral pretreatment selected for developing the calibration model. Partial least squares regression was used to establish the model in order to predict the water activity. The results showed the accuracy of the calibration model in prediction that gave a correlation coefficient of 0.767 and the root mean square error of prediction of 0.0130. It was therefore concluded that NIR hyperspectral imaging has a potential for use and application for measuring the water activity of mamón cakes.
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Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch
(2021-05-01)
Khamsopha, Duangkamolrat
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Woranitta, Sahachairungrueng
;
Teerachaichayut, Sontisuk
Fraud creates huge problems for the food industry. One type of fraud is adulteration in order to reduce costs and increase profitability. Fraud occurs in the starch industry, which is difficult or impossible to detect by visual inspection. Therefore this study was to test a possible nondestructive method that could be used to detect the adulterants in tapioca starch by utilizing reflectance near infrared hyperspectral imaging (NIR-HSI) at wavelengths in the range of 935–1720 nm. Pure tapioca starch was adulterated with limestone powder at 0.5% intervals over the range of 0–100% (wt/wt). The samples (n = 201) were divided into a calibration set (n = 140) and a prediction set (n = 61). Chemometrics was investigated and used to establish a calibration model for predicting the concentration of adulterant using partial least squares regression (PLSR). The accuracy of prediction using the model gave excellent results with the correlation coefficient (R) of 0.996 and the root mean square error of prediction (RMSEP) of 2.47%. The model was then used to create the predictive images of pure tapioca starch, adulterated tapioca starch and pure adulterant. It showed different colors based on the concentration of the adulterant. Therefore, NIR-HSI was shown to have potential as a method for rapidly detecting the level of concentration of adulterant in tapioca starch using both the predictive model and visualization.
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Quantitative analysis of quality for marian plum (Bouea burmanica Griff.) by transmittance near infrared spectroscopy
(2018-10-05)
Phonmakham, S.
;
Suttivijitpukdee, N.
;
Teerachaichayut, S.
Marian plum (Bouea burmanica Griff.) is one of the most popular tropical fruits in Thailand. The good quality of marian plum is required by consumers. Total soluble solid (TSS) and titratable acidity (TA) are important indices for consideration of quality for marian plum. Transmittance mode of near infrared (NIR) spectroscopy in the short wavelength (665-955 nm) was considered for nondestructive evaluation of quality in marian plum. A set of 153 marian plums (105 samples for a calibration group and 48 samples for a prediction group) was carried out in this research. The partial least squares regression (PLSR) was used to develop the calibration models. Spectral pretreatments were investigated in order to obtain the best performance of the models. A calibration model for TSS using original spectra obtained best results for calibration and prediction (R=0.90, RMSEC=0.57 °Bx and R=0.88, RMSEP=0.65 °Bx, respectively). As well as the calibration model for TA using original spectra obtained best results for calibration and prediction (R=0.98, RMSEC=0.01% and R=0.88, RMSEP=0.03%, respectively). All results indicated that it is possible to use transmittance SW-NIRS for nondestructive prediction of TSS and TA in marian plums.

Teerachaichayut, Sontisuk

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