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Basal stem rot (BSR) is a prominent plant disease caused by Ganoderma boninense fungus, which infects oil palm plantations leading to large economic losses in palm oil production. There is need for novel disease detection techniques that can be used to reduce the oil palm losses due to BSR. Thus, this paper investigated the feasibility of utilizing electrical properties such as impedance, capacitance, dielectric constant, and dissipation factor in early detection of BSR disease in oil palm tree. Leaf samples from different oil palm trees (healthy, mild, moderate, and severely-infected) were collected and measured using a solid test fixture (16451B, Keysight Technologies, Japan) connected to an impedance analyzer (4294A, Agilent Technologies, Japan) at a frequency range of 100 kHz-30 MHz with 300 spectral interval. Genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS) were used to analyze the electrical properties of the dataset and the most significant frequencies were selected. Following the selection of significant frequencies, the features were evaluated using two classifiers, support vector machine (SVM) and artificial neural networks (ANN) to determine the overall and individual class classification accuracies. The selection model comparative feature analysis demonstrated that the best statistical indicators with overall accuracy (88.64%), kappa (0.8480) and low mean absolute error (0.1652) were obtained using significant frequencies produced by SVM-FS model. The results indicated that the SVM classifier shows better performance as compared to ANN classifier. The results also showed that the classes, features selection models, and the electrical properties were found to be significantly different (p < .1). The impedance values were highly classified by Ganoderma disease at different levels of severity with overall accuracies of more than 80%. Impedance can be considered as the best electrical properties that can be used to estimate the severity of BSR disease in oil palm using spectroscopy technique. As such, this study demonstrates the potentials of utilizing electrical properties for detection of Ganoderma diseases in oil palm.

Meat is one of the most consumed agro‐products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor‐intensive, time‐consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e‐nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e‐nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

This study examines the potential of applying computational intelligence modelling to describe the drying kinetics of persimmon fruit slices during vacuum drying (VD) and hot-air-drying (HAD) under different drying temperatures of 50 °C, 60 °C and 70 °C and samples thicknesses of 5 mm and 8 mm. Kinetic models were developed using selected thin layer models and computational intelligence methods including multi-layer feed-forward artificial neural network (ANN), support vector machine (SVM) and k-nearest neighbors (kNN). The statistical indicators of the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the suitability of the models. The effective moisture diffusivity and activation energy varied between 1.417 × 10−9 m2/s and 1.925 × 10−8 m2/s and 34.1560 kJ/mol to 64.2895 kJ/mol, respectively. The thin-layer models illustrated that page and logarithmic model can adequately describe the drying kinetics of persimmon sliced samples with R2 values (>0.9900) and lowest RMSE (<0.0200). The ANN, SVM and kNN models showed R2 and RMSE values of 0.9994, 1.0000, 0.9327, 0.0124, 0.0004 and 0.1271, respectively. The validation results indicated good agreement between the predicted values obtained from the computational intelligence methods and the experimental moisture ratio data. Based on the study results, computational intelligence methods can reliably be used to describe the drying kinetics of persimmon fruit.

Abstract-When the cooking oil is used repeatedly, several unwanted substances are generated, which may cause health problems. This study was conducted to determine the possibility of using the impedance spectroscopy to differentiate among varying cooking oil quality at various intervals of heating time at constant temperature. The frequency has started from 100 Hz to 100kHz. Fresh, 10-hour, 20-hour, 30-hour, and 40-hour heated cooking oil was prepared by using lab oven at temperature of 180oC. In this study, a sensing probe was designed to measure the electrical properties of the oil samples. The oil samples were analyzed using a viscometer to measure the viscosity of the oil, a sensor to measure total polar compound (TPC), and an impedance probe connected to a LCR meter to measure the electrical properties of the oil. The measurements were analyzed and correlated with oil quality parameters obtained from a viscometer and a sensor of TPC. The discrimination between different heated hours of oil samples was examined and the results were compared to their physico-chemical properties such as viscosity and total polar compounds. The effect of heating of frying oils were successfully evaluated and discriminated using the impedance spectroscopy. Significant correlations (r -0.98472) were found between changes in total polar compound properties of oil and the impedance values.

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Total polar compounds (TPC) and free fatty acids (FFA) are important indicators in evaluating the quality of frying oil. Conventional methods to determine TPC and FFA are often time consuming, involved laboratory analyses which required skilled personnel and used substantial amount of harmful solvent. In this study, dielectric spectroscopy technique was used to investigate the relation between dielectric property of refined, bleached and deodorized palm olein (RBDPO) during deep frying with TPC and FFA. In total, 150 batches of French fries were intermittently fried at 185 ± 5 °C for 7 hr a day over 5 consecutive days. A total of 30 frying oil samples were collected. The dielectric property of frying oil samples were measured using impedance analyzer with frequencies ranging from 100 Hz to 10 MHz. The TPC of frying oil samples were measured with a Testo 270, while the FFA analysis was done using Malaysian Palm Oil Board (MPOB) test method. Results showed that dielectric constant, TPC and FFA of RBDPO increased as the frying time increased. Dielectric constant increased from 3.09 to 3.17, while TPC and FFA increased from 9.96 to 19.52 and from 0.08% to 0.36%, respectively. Partial least square (PLS) analysis produced good prediction of TPC and FFA with the application of genetic algorithm (GA). Model developed for prediction of TPC and FFA yielded highly significant correlation with R2 of 0.91 and 0.95, respectively and both had root mean square error in cross‐validation (RMSECV) of 1.06%. This study demonstrates the potential of dielectric spectroscopy in monitoring palm olein degradation during frying. Practical Application The application of dielectric spectroscopy to detect degradation of palm olein during frying was studied. The dielectric property of palm olein during frying has successfully correlated with TPC and FFA. The model developed in this study could be used for the development of a sensing system for palm olein degradation monitoring.