KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 7 of 7

Some of the metrics are blocked by your
consent settings
Rapid detection of potassium sorbate in coconut water using near infrared hyperspectral imaging
(2026-01-01)
Tantinantrakun, Achiraya
;
Kumpa, Benjaporn
;
Ainkast, Pranpriya
;
Thompson, Anthony Keith
;
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.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch
(2021-05-01)
Khamsopha, Duangkamolrat
;
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.
Some of the metrics are blocked by your
consent settings
Prediction of water activity in mamón (Filipino sponge) cakes by near infrared hyperspectral imaging
(2020-01-01)
Sricharoonratana, Manunchaya
;
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.
Some of the metrics are blocked by your
consent settings
Detection of adulteration of tapioca starch with dolomite by near infrared hyperspectral imaging
(2020-01-01)
Khamsopha, Duangkamolrat
;
Teerachaichayut, Sontisuk
Tapioca starch adulterated with dolomite is sold in markets, but this adulteration cannot be identified by normal visual inspection. Near infrared (NIR) hyperspectral imaging has been successfully used as a non-destructive method of identifying various characteristics of food, therefore it was tested to identify dolomite adulteration. Adulterated tapioca starch samples were prepared by adding dolomite in the range of 0.5-100% (wt/wt). Samples (N=400) of pure tapioca starch (0) and adulterated tapioca starch (1) were divided into calibration set (N=300) and a prediction set (N=100). All samples were scanned using NIR hyperspectral imaging (935-1720 nm) and spectra were pre-processed using Savitzky-Golay first derivative differentiation pretreatment in order to obtain the optimal conditions for establishing a classification model. Partial least squares-discriminant analysis was carried out to evaluate the accuracy of classification tapioca starch adulterated with dolomite. The results showed the total accuracy of prediction for classification was 100%. Therefore, NIR hyperspectral imaging was demonstrated to have a potential for use in detecting adulteration of tapioca starch with dolomite.
Some of the metrics are blocked by your
consent settings
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.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results