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Rapid detection of potassium sorbate in coconut water using near infrared hyperspectral imaging
(2026-01-01)
Tantinantrakun, Achiraya
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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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Assessing adulterated pineapple juice concentrate using electrical properties
(2025-01-01)
Tantinantrakun, Achiraya
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Sinsamut, Varisara
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Apairat, Nuengruthai
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Smutrakalin, Thirapol
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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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Calibration of Bi-prism Stereo Systems: A Model Free Approach
(2024-07-01)
Dissanayaka, Supun
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Sooraksa, Pitikhate
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Kaitwanidvilai, Somyot
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Morris, John
A simple stereo system can be constructed from a single camera using a prism in the optical path to provide the required two views of a system. The simplicity of these systems has several advantages, particularly if the target is an underwater robot, where compact size and ability to seal the optical components are key factors. However, dispersion by the prism, in addition to the lens distortion, makes calibration challenging. By using a model-free approach, we were able to calibrate a prism-based stereo system effectively. We also aimed to use readily available 45° prisms, which present significant dispersion in the system, but retain simplicity and reduce cost, compared to custom low angle prisms. Modern LEDs provide high intensity, low bandwidth light sources and we used a set of three sources, roughly centered on the RGB channels of a readily available commercial camera. Our system used a circular target pattern covering the binocularly visible region in the scene and collected sets of images at known distances, using three separate light sources. From these images, we generated two look-up tables, one for each pixel in the image and a disparity derived by matching corresponding points, Cp(u,v,du), which has three dimensions, and another look-up table, which has a single dimension, Cz(z), so are not quite large, and not beyond the memory capability of even small modern camera systems, but provide fast, O(1), lookup times, suitable for real-time systems. Our calibration strategy enables a simple stereo system built from a single camera to measure depths in a scene: the single camera requires no electronic synchronization and is built from a single, inexpensive, and readily available optical component – a right-angle prism.
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Design, calibration, and validation of an inline green coffee moisture estimation system using time-domain reflectometry
(2023-03-01)
Anokye-Bempah, Laudia
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Phetpan, Kittisak
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Slaughter, David
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Donis-González, Irwin R.
Wet basis moisture content (MC<inf>wb</inf>) is an important quality parameter of green coffee as it affects the coffee's physical, chemical, and sensory characteristics. Accurate estimation of green coffee MC<inf>wb</inf> after dry hulling, long-term storage, and transportation is imperative to prevent quantitative and qualitative losses. Thus, this study aimed to design, develop, calibrate and validate a prototype inline system capable of accurately measuring the MC<inf>wb</inf> of green coffee beans, using a commercially available time-domain reflectometry (TDR) probe. The TDR probe was calibrated and validated with green coffee within a MC<inf>wb</inf> range of 9–21%. A calibration linear regression model correlating the TDR probe output (dielectric constant) to reference MC<inf>wb</inf> measurements obtained by a halogen moisture analyzer, yielded a high coefficient of correlation (R<sup>2</sup> = 0.99). Model validation yielded a high R<sup>2</sup>, and a low Root Mean Squared Error equal to 0.93, and 0.9% MC<inf>wb</inf>, subsequently. Results indicate that the TDR inline green coffee moisture estimation system has the potential to be applied in real-time, industrial-scale operations.
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Small scale method for estimation of genetic coefficients of photoperiod-insensitive rice using generalized likelihood uncertainty estimation
(2023-03-01)
Suanphrom, Nattawut
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Khurnpoon, Lampan
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Phakamas, Nittaya
Importance of the work: Genetic coefficients are important parameters for simulations of rice yield performance in crop growth models. Most genetic coefficients (GCs) are obtained from large experiments. Objectives: To estimate the GCs of seven photoperiod-insensitive rice cultivars for four planting dates in a pot experiment. Materials & Methods: Input data (soil, weather, management, plant parameters) were collected and used to calibrate the GCs of seven rice cultivars using the GLUE estimator in the DSSAT version 4.7 package. The data were collected from four planting dates: 1) 23 Nov 2019; 2) 23 Dec 2019; 3) 23 Jan 2020; and 4) 23 Feb 2020. The data from planting dates 1, 3 and 4 were used for calibration of the GCs, whereas the data from planting date 2 were used for evaluation of the GCs. Results: Good prediction qualities of the model for most cultivars were indicated for days to anthesis and days to physiological maturity; however, there were poor prediction qualities for almost all cultivars for their biomass and grain weight. Main finding: This information should be useful for further investigations of GCs in rice. Although the results were contrary to the initial hypothesis, the method showed promise for further use in rice modeling research if the method can be improved by experimental management, the use of suitable reference plants for each cultivar and running the model for an appropriate cycle. It was possible to obtain some reliable GCs from small-scale experiments, so the experiment should be improved to obtain better results. Further investigations should focus on the optimum scale and weather data specific to experimental sites.
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PERMEABILITY OF THE DIKE 1’S MATERIALS OF KAENG KRACHAN DAM, THAILAND
(2022-02-01)
Sirikaew, Uba
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Hopeancharoen, Witthawin
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Seeboonruang, Uma
Dike 1 of Kaeng Krachan Dam located in Phetchaburi province of Thailand constructed in 1966 with a reservoir capacity of 710 million m3. This large-scale project provides more than 55 years of irrigation and flood protection. Risk evaluation of dam is needed to be performed. The calibration of engineering properties of the Dike 1 is conducted because there is no database of those properties. The existing Dike 1 cross-section is a soil model, used for calculation. Piezometric level and flow rate obtained from the dam instruments were calibrated with the hydraulic head and the flow rate was determined by the SEEP/W model. The permeability coefficient of the Dike 1 materials can be analyzed by the calibration technique. The data of the dam instruments are helpful information, and the computer program is friendly to use. The coefficient of permeability of the soil of the Dike 1 of Kaeng Krachan Dam is determined and applied to risk analysis.
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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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Detection of adulteration of tapioca starch with dolomite by near infrared hyperspectral imaging
(2020-01-01)
Khamsopha, Duangkamolrat
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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.

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