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Identification and quantification of quality of intact durian fruits using NIR spectroscopy
(2026-01-01)
Pitak, Lakkana
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Ditcharoen, Sirirak
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Maraphum, Kanvisit
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Khamwan, Buathip
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Warorost, Nithithada
Quality classification of durian fruits is based on the dry matter (DM) content of the pulp. According to Thai agricultural standards, durian fruit (Monthong variety) must contain at least 32% DM. This study aimed to develop a classification model for assessing durian quality based on DM content, categorizing fruits as either “rejected” (DM < 32%) or “accepted” (DM ≥ 32%). Near-infrared (NIR) spectra were collected as the durian fruits moved along a conveyor belt. The models were developed using two spectral ranges: short-wavelength near-infrared (SWNIR; 4501000 nm) and long-wavelength near-infrared (LWNIR; 8601750 nm). Owing to the imbalance in the dataset between the two classes, the data were adjusted using the synthetic minority oversampling technique to create a balanced dataset. Prediction models were built using different spectral preprocessing methods and algorithms. For the LWNIR range, the models constructed using LDA, SVM, KNN, and SDA achieved accuracies of 95%, 90%, 93%, and 93%, respectively, for the test set. The SWNIR models, developed using the same algorithms, achieved accuracies of 90%, 88%, 90%, and 90%, respectively, for the test set. PLS-regression was used to predict the DM content from both LWNIR and SWNIR data. With the 2nd derivative preprocessing method, the models achieved R² values of 0.89 and 0.79, SEP values of 5% and 6.89%, and RPD values of 2.29 and 1.66, respectively. The wavelength range significantly influenced the model performance, whereas spectral pretreatment had a minor effect on the model's predictive ability. Overall, NIR spectroscopy demonstrated the potential for nondestructive quality grading of whole durian fruits. This work is the first to establish real-time, in-line models for durian grading based on DM content, advancing beyond the previous destructive method. The findings demonstrate the feasibility of automated, nondestructive, and objective quality assessment, supporting industrial automation, precision agriculture, and export quality assurance.
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Deep neural networks (DNNs) chemical compositions estimation of fresh durian in-line via near infrared spectroscopy
(2025-06-01)
Posom, Jetsada
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Saenphon, Chirawan
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Ditcharoen, Sirirak
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Pitak, Lakkana
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Sirisomboon, Panmanas
Near infrared (NIR) spectroscopy and deep neural network (DNN) models were adopted for evaluating the nutritional compositions in durian pulp. The quality inspection of durians through online channels remains challenging because consumers cannot directly touch or smell the fruit. This leads to issues with substandard durians, such as unripe ones or those infested with pests. One hundred and sixty durian samples of Mon Thong varieties were used in this experiment. Then, the collected NIR spectra were augmented to improve the generalization ability of regression models. Deep neural network (DNNs) regressions were developed. The results showed that deep neural network (DNN) regression model possessed the best prediction performance, which were provided performance index. Almost all the best performance models were developed from the second derivative, except for the fat content model, which was developed from raw spectra. The best total soluble solids (TSS) model provided the coefficient of determination of calibration (R2c) and root mean square error of calibration (RMSEc) were 0.84 and 2.25 % and coefficient of determination of calibration (R2p), root mean square error of prediction (RMSEp) and ratio of performance to deviation (RPD) were 0.72 2.92 % and 1.92, respectively, and those of dry matter content (DMC) were provided R2c and RMSEc were 0.997, 0.58 %, and for prediction set provided R2p, RMSEp and RPD were 0.94, 3.13 %, and 4.21, respectively. For fat content (FC), they have also provided R2c and RMSEc were 0.84, 0.36 (g/100 g), while it provided R2p, RMSEp and RPD were 0.86, 0.50 (g/100 g), and 2.72, respectively. Moreover, for total sugar content (TSC) value, it also gave high accuracy, which provided R2c and RMSEc were 0.93, 0.49 (g/100 g) and R2p, RMSEp, and RPD were 0.91, 0.81 (g/100 g), 3.44, and for starch content (SC) also provided R2c and RMSEc were 0.93, 0.49(g/100 g) and R2p, RMSEp, and RPD were 0.76, 2.79 (g/100 g), 2.09, respectively. Therefore, the proposed method offers an ultrasensitive and effective strategy for estimation of nutritional compositions.
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Yield Response of Sweet Potato (Ipomoea batatas L.) Genotypes Cultivated in Post-Rice Paddy Field
(2025-01-01)
Ruttanaprasert, Ruttanachira
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Pinta, Wanwipa
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Pitak, Lakkana
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Janket, Anon
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Bunphan, Darika
Sweet potatoes (Ipomoea batatas L.) are a nutrient-rich root crop with substantial nutritional, economic, and environmental benefits. This study assessed nine sweet potato genotypes across three distinct locations in Thailand to evaluate yield potential, adaptability, and environmental influences. Trials used a randomized complete block design with four replications. Location 1 (Thatum District) featured sandy soil with moderate pH, high salinity, and balanced nutrients but lower phosphorus (P) and nitrogen (N). Location 2 (Maung District) had sandy, acidic soil but was the most chemically fertile, with high P and potassium (K). Location 3 (Rajamangala University of Technology Isan, Surin Campus) had loamy sand with higher silt content, acidic pH, low P, K, and electrical conductivity (EC). Results highlighted significant yield differences influenced by soil and environmental variability. Genotype SR18003 achieved the highest yield and harvest index, demonstrating strong adaptability and commercial potential. Strong correlations between storage root traits (number, size, and weight) indicate their usefulness for selection. The study underscores the importance of tailored soil management and location-specific genotypes in optimizing sweet potato production, contributing to resilient varieties, sustainable farming, and improved food security. Further research on genetics and agronomy can enhance breeding and cultivation systems.
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Near-infrared spectroscopy, hyperspectral, multispectral imaging principles and applications in energy properties of biomass
(2023-08-21)
Posom, Jetsada
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Shrestra, Bijendra
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Maraphum, Kanvisit
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Pitak, Lakkana
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Saengprachatanarug, Khwantri
Biomass is renewable energy which is zero neutrality carbon energy. It is used for generating heat energy and electrical energy. Therefore, the use of biomass with high efficiency is important and the quality of biomass related to its energy should be measured before utilization and trading. The measurement of energy indexes of biomass is necessary to the thermal conversion process and the trading of biomass. However, the conventional measurement methods are laborious and take a long time, with a lot of costs. In recent years, near infrared spectroscopy (NIR) and imaging technologies (hyperspectral and multispectral images) have been widely investigated and applied as non-destructive, reliable and accurate techniques to monitor the quality and composition of biomass. This chapter contains the principle of NIR and imaging technique including essential component principles, NIR and imaging technique procedures, novel model development methods and applications. The non-destructive measurement of biomass quality as the real time and non- contact measurement will be represented. Moreover, this chapter will describe the application of NIR and imaging techniques for analysing the energy indexes of biomass, such as heating value or calorific value, proximate data, elemental composition, combustion index, pyrolysis characteristics, mechanical properties and so on.
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Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis
(2021-04-01)
Pitak, Lakkana
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Sirisomboon, Panmanas
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Saengprachatanarug, Khwantri
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Wongpichet, Seree
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Posom, Jetsada
The use of biomass pellets as a renewable energy source is increasing, leading to the need for rapid assessment of biofuel pellet quality for production monitoring. The purpose of this work was to use line-scan near-infrared (NIR) hyperspectral image technology coupled with chemometric tools to assess the elemental components of biomass pellets. The parameters influencing model performance were investigated, i.e. wavelength and spectral pretreatment technique. Either full wavelength or partial wavelength selected using interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA) were investigated. Either raw spectra or pretreated spectra were used for model development. The models were developed using partial least squares regression (PLSR). The most effective model for the prediction of carbon (C), hydrogen (H), and nitrogen (N) content was developed using iGA wavelength selection and standard normal variate (SNV) spectral pretreatment and provided the highest accuracy with a coefficient of determination of prediction set (r2<inf>p</inf>) and standard error of prediction (SEP) of 0.83 and 1.33%; 0.84 and 0.17%; and 0.90 and 0.098%, respectively. The model could be used for quality assurance. The S content model was poor and not recommended. The relationship between pellet chemical parameters and reflectance characteristics could be used for predicting C, H, and N of biomass pellets.
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Machine learning-based prediction of selected parameters of commercial biomass pellets using line scan near infrared-hyperspectral image
(2021-02-01)
Pitak, Lakkana
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Laloon, Kittipong
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Wongpichet, Seree
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Sirisomboon, Panmanas
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Posom, Jetsada
Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

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