Teerachaichayut, Sontisuk

Preferred name

Alternative Name

Teerachaichayut, S.

Main Affiliation

School of Food Industry

sontisuk.te@kmitl.ac.th

Now showing 1 - 7 of 7

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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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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.
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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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Minimally destructive assessment of mangosteen translucency based on electrical impedance measurements
(2016-02-01)
Nakawajana, Natrapee
;
Terdwongworakul, Anupun
;
Teerachaichayut, Sontisuk
Electrical impedance spectroscopy in a frequency range of 1 kHz-200 kHz was studied to develop a classifying model for translucent mangosteen. The optimal configuration of the measurement was investigated. Transverse alignment of two measuring needles with the stem-calyx axis and with the measured position on the part of the pericarp pertinent to the largest flesh segment proved to be the optimal configuration. The optimal electrical parameters were selected at frequencies of 1, 4, 7, 8, 14, 47, 73, and 81 kHz as the classifying variables based on the student t-test analysis for a significant difference between the normal and translucent mangosteen and the largest difference of the average values of the electrical parameters. The differences in the electrical parameters and their reciprocals were the optimal classifying variables. The model constructed from the samples from two seasons was robust in terms of seasonality, providing a classification accuracy of 82.7%. The difference in the initial moisture content of the pericarp was justifiably compensated by the differences in the electrical parameters. The EIS technique was suitable for measurement of mangosteen samples at the maturity color stage in which the sample contained no yellow latex in the pericarp.
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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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Quantitative prediction of nitrate level in intact pineapple using Vis-NIRS
(2015-01-01)
Srivichien, Sasathorn
;
Terdwongworakul, Anupun
;
Teerachaichayut, Sontisuk
Before pineapples are canned, the ones with high nitrate level must be sorted out first because nitrate causes black stains on the surface of the can; therefore, a nondestructive technique for sorting out pineapples is clearly needed. The use of visible and near infrared (Vis-NIR) spectroscopy for such purpose was investigated in this study. A batch of 75 pineapple fruits that would have been delivered to a canning factory was tested. Spectra were acquired using a spectrophotometer in interactance mode with wavelengths in the region of 400-2500 nm. Twelve scans of different parts of each pineapple were made. The actual amount of nitrate in the pineapple flesh was determined by HPLC. Original spectra and pretreated spectra were both used to construct calibration models with partial least squares regression (PLSR). The best model was obtained from an average spectrum pretreated with first derivative treatment at the wavelength range of 600-1200 nm. Predictions based on this model matched closely with the actual nitrate contents, with a high correlation coefficient (R) of 0.95 and a low root mean square error of prediction (RMSEP) of 1.77 ppm. These results demonstrate that Vis-NIR spectroscopy can be used for rough screening of intact pineapple.
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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.

Teerachaichayut, Sontisuk

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