Multispectral imaging for predicting the water status in mushroom during hot-air dehydration

Moisture variation analysis of the green plum during the drying process based on low-field nuclear magnetic resonance

Green plums were dried at 50, 60, 70, and 80 ℃ to study the dynamic changes of internal moisture during the drying process. Low-field nuclear magnetic resonance (LF-NMR) was used to study the dynamic changes across the T₂ relaxation spectrum, while magnetic resonance imaging (MRI) provided visualization of the plums throughout the process. The results indicate a negative linear relationship between the lost moisture of the plums (p < 0.05) as drying time increased. Relaxation times T₂₁ , T_22, and T₂₃ , and the peak areas of A₂₁ and A₂₃ decreased significantly during the drying process. The MRI results also show that the brightness of the images decreased as the drying time increased, indicating that the higher the temperature, the greater the water loss inside the plums. Color measurements demonstrated that the high temperature dried plums had better sensory quality. Correlation analysis implies a strong positive relationship between A₂₃ and A_total and water content, with coefficients of 0.958 and 0.936, respectively. Principal component analysis results show that the drying temperature has a significant effect on the sample's internal moisture release. LF-NMR is a fast, convenient, and feasible technique for monitoring the moisture variation of green plums during the drying process. PRACTICAL APPLICATION: Low-field nuclear magnetic resonance (LF-NMR) was used to study the moisture dynamic changes of green plums across the T₂ relaxation spectrum, while magnetic resonance imaging (MRI) provided visualization of plums throughout the process. The drying temperature has a significant effect on the green plum's internal moisture release and may affect the quality of the plums. LF-NMR might be a complementary technique in monitoring the moisture variation of green plums during the drying process.

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.

Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes

Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.

Effects of cold air dehydration on icefish water dynamics and macromolecular oxidation measured by low-field nuclear magnetic resonance and magnetic resonance imaging

We have used low-field nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging to measure water dynamics and migration, color, and texture profile (TPA) of icefish dried with hot and cold air methods. Relaxation time of T21, T22, and T23, and the peak area of A22 and A23 decreased significantly during drying. The water signal intensity decreased from the surface to inner regions during drying. Color parameters of L* and b* values increased significantly, TPA parameters of hardness increased, cohesiveness decreased significantly, and moisture content decreased significantly during drying. We observed correlations between the moisture content, TPA, color, and NMR parameters. In addition, we found lower thiobarbituric acid reactive substances and carbonyl content of the dried icefish with cold air compared with hot air. The cold air drying method yielded better sensory quality, and LF-NMR was a useful nondestructive method to determine the degree of drying and the quality of icefish.