Late blight caused by Phytophthora infestans is a destructive disease in potato production, which can lead to crop failure in severe cases. This study combined visible/near-infrared (Vis/NIR) spectroscopy with machine learning (ML) and chemometric methods for rapid detection of potato late blight. The determination of disease severity was accomplished by two methods directly or indirectly based on differences in reflectance. One approach was to utilize ML algorithms to build a model that directly reflects the relationship between disease level and spectral reflectance. Another method was to first use partial least squares to construct a predictive model of internal physicochemical values, such as relative chlorophyll content (SPAD) and peroxidase (POD) activity, and then use an ML model to classify disease levels based on the predicted values. The classification accuracy based on these two methods could reach up to 99 and 95%, respectively. The changes in physicochemical values during the development of disease were further investigated. Regression models for fitting changes in SPAD value and POD activity were developed based on temperature and incubation time, with determination coefficients of 0.961 and 0.997, respectively. The prediction of epidemic period was realized by combining regression and classification models based on physicochemical values with an accuracy of 88.5%. It is demonstrated that rapid non-destructive determination of physicochemical values based on Vis/NIR spectroscopy for potato late blight detection is feasible. Furthermore, it is possible to guide the control of disease throughout the epidemic period.

Knowledge of bacteria’s heat resistance is essential for developing effective thermal treatments. Choosing an appropriate test method is important to accurately determine bacteria’s heat resistances. Although being a major factor to influence the thermo-tolerance of bacteria, the heating rate in samples cannot be controlled in water or oil bath methods due to main dependence on sample’s thermal properties. A heating block system (HBS) was designed to regulate the heating rates in liquid, semi-solid and solid foods using a temperature controller. Distilled water, apple juice, mashed potato, almond powder and beef were selected to evaluate the HBS’s performance by experiment and computer simulation. The results showed that the heating rates of 1, 5 and 10 °C/min with final set-point temperatures and holding times could be easily and precisely achieved in five selected food materials. A good agreement in sample central temperature profiles was obtained under various heating rates between experiment and simulation. The experimental and simulated results showed that the HBS could provide a sufficiently uniform heating environment in food samples. The effect of heating rate on bacterial thermal resistance was evaluated with the HBS. The system may hold potential applications for rapid and accurate assessments of bacteria’s thermo-tolerances.

Knowledge of the thermal and dielectric properties of wolfberries is essential for understanding the heat transfer and the interaction between the electromagnetic field (10–3000 MHz) and the sample during radio frequency (RF) and microwave (MW) drying. The thermal and dielectric properties of wolfberries were determined as influenced by moisture content from 15.1% to 75.2%, w.b.) and temperature from 25 to 85 °C. The results showed that as the moisture content increased from 15.1% to 75.2% (w.b.), the true density of wolfberries decreased, but the specific heat capacity and thermal conductivity increased with increasing temperature and moisture content. The dielectric properties (DPs) of wolfberries decreased with increasing frequency from 10 to 3000 MHz. The dielectric constant increased with increasing temperature at lower a moisture content (below 45% w.b.) but decreased with increasing temperature at a high moisture content (above 60% w.b.). The cubic and quadratic polynomial models (R² = 0.977 − 0.997) were best for fitting the dielectric constant and loss factor at four representative frequencies of 27, 40, 915, and 2450 MHz, respectively. The penetration depth increased with the decreased frequency, temperature, and moisture content, and was greater at RF frequencies than MW range, making the RF heating more effective for drying bulk wolfberries. These findings offered essential data before optimizing RF or MW dehydration protocols for wolfberries via computer simulation.

Thermal treatment has been extensively used to control pests in stored grains for a long time. The objective of this study was to analyze thermal death kinetics of adult flat grain beetle, Cryptolestes pusillus (Schonherr), lesser grain borer, Rhyzopertha dominica (Fabricius), and confused flour beetle, Tribolium confusum (Jacquelin du Val), using a heating block system (HBS), at temperatures of 46, 48, 50, and 52 °C for C. pusillus and T. confusum, and 48, 50, 52, and 54 °C for R. dominica with a heating rate of 5 °C/min. Thermal death curves of those three insects followed a 0th-order reaction model. Complete mortality of C. pusillus, R. dominica, and T. confusum were observed after exposure to 1.4, 5.0, and 0.9 min at 52, 54 and 52 °C, respectively. The thermal death activation energy for controlling C. pusillus, R. dominica, and T. confusum was 689.91, 380.88, and 617.08 kJ/mol with z values of 2.88, 5.18, and 3.22 °C, respectively. The cumulative lethal time model can also be used to predict mortality of these three insects during a practical heating process. The information provided by this study on storage pests may be useful for developing effective thermal treatment protocols.