Efficacy study on the non-destructive determination of water fractions in infrared-dried Lentinus edodes using multispectral imaging

A study into how thickness, infrared intensity, and airflow affect drying kinetics, modeling, activation energy, and quality attributes of apple slices using infrared dryer

Drying is a widely recognized process that reduces the need for storage and shipping weight, keeps free water out of the product, and prolongs its shelf life. An infrared dryer was designed to dry apples under different drying conditions. Apple slices of 6-, 4-, and 2-mm thicknesses were dried at intensities 0.130, 0.225, and 0.341 W/cm2 and airflow 1.0, 0.5, and 1.5 m/s. The dehydrating period was prolonged with higher airflow and shortened with higher infrared intensity (IR). The shortest dehydrating period was verified by 190 min at 0.341 W/cm2, 0.5 m/s under 2 mm thickness. Increasing the sample thickness from 2 to 4 mm and then to 6 mm resulted in an 84% and 192% increase in drying time, respectively. Dehydrated apples had water activity values ranging from 0.30 to 0.40. The shrinkage ratio increased with an increase in infrared radiation intensity. However, it decreased with an increase in air velocity, while the rehydration ratio decreased with an increase in radiation intensity and increased with an increase in air velocity. Regarding total color change, apple slice thickness was a major factor. The effective diffusivities varied between 2.6 and 9.0 𝗑10-10 m2/s under different drying conditions. The dehydrating curves of apples were best described by the model developed by Midilli et al.

Effect of postharvest storage time on quality characteristics of explosion puffing dried whole shiitake mushroom (Lentinula edodes) crisps

BACKGROUND

Non-fried shiitake mushroom (Lentinula edodes) crisps fabricated by explosion puffing drying (EPD) are receiving worldwide attention because of their crispness, convenience, nutrition and health functions. The quality of mushroom crisps varies with storage time of fresh L. edodes. Therefore, the effect of postharvest storage time (ranging from 0 to 14 days) of fresh L. edodes on quality characteristics of EPD- processed mushroom crisps was evaluated.

RESULTS

The weight loss and total color difference of fresh L. edodes were increased to 2.95% and 24.66, but moisture content, firmness and lightness were reduced by 6.14%, 40.70% and 43.57%, respectively, after 14 days storage. The puffing degree of mushroom crisps was initially increased to its highest value (55.95%) on the 4th day storage and thereafter decreased. The highest rehydration ratio (2.36) and crispness (63.67), and lowest hardness (102.95 N) of mushroom crisps were fabricated with L. edodes on the 4th day of storage. Free water was predominant in fresh L. edodes, which was decreased for fresh L. edodes, whereas it increased initially to the maximum value and decreased thereafter for osmotic dehydrated and heat pump pre-dried L. edodes with increasing storage time. Principal component analysis and hierarchical cluster analysis confirmed that fresh L. edodes stored at different times had a remarkable effect on quality characteristics of mushroom crisps.

CONCLUSION

Fresh L. edodes stored at 4 ± 1 °C for 4 days is recommended for fabrication of mushroom crisps with superior quality. This study provides a theoretical basis for selection of a suitable storage time for fresh L. edodes before EPD of crisps. © 2023 Society of Chemical Industry.

Toward commercial applications of LED and laser-induced fluorescence techniques for food identity, quality, and safety monitoring: A review

The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco-friendly, and non-destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation-emission matrix (EEM) has been extensively explored in the food industry, but on-site detection of EEMs remain a challenge. To address this limitation, laser-induced fluorescence (LIF) and light emitting diode-induced fluorescence (LED-IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED-IF devices for non-destructive food measurement and recent studies that focus on the development of LIF/LED-IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre-application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low-concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

New preservation and detection technologies for edible mushrooms: A review

Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.

Nondestructive detection of water status and distribution in corn kernels during hot air drying using multispectral imaging

BACKGROUND

The characteristics of corn kernels are strongly connected with the content of three statuses of water: bound water, immobilized water, and free water. Monitoring different water contents is very important to optimize the drying process, improve corn quality, and reduce energy consumption. The feasibility of nondestructive detection of water status and its distribution in corn kernels during the hot-air drying process using multispectral imaging was investigated.

RESULTS

The chemometric methods used to develop prediction models were back propagation neural network, least-squares support vector machine, and partial least squares. The back propagation neural network achieved the best prediction performance for total and free water contents, with correlation coefficient of prediction (R_p ) of 0.9717 and 0.9782 respectively, root-mean-square error of prediction (RMSEP) of 4.48% and 2.54% respectively, and ratio of prediction to deviation (RPD) of 4.87 and 4.29 respectively. And partial least squares was better for the prediction of immobilized and bound water contents, with R_p of 0.9612 and 0.9798 respectively, RMSEP of 0.57% and 0.06% respectively, and RPD of 4.78 and 4.42 respectively.

CONCLUSION

It could be concluded that multispectral imaging combined with chemometric methods would be a promising technique for rapid and nondestructive detection of water status and its distribution in corn kernels. © 2023 Society of Chemical Industry.

Enhancing drying efficiency and quality of seed-used pumpkin using ultrasound, freeze-thawing and blanching pretreatments

Effects of blanching (BL), ultrasound (US) and freeze-thawing (FT) pretreatments prior to far-infrared drying (FIRD) on drying characteristics, water distribution, and quality parameters of seed-used pumpkin (SUP) slices were investigated in this study. US, BL and FT pretreatments significantly accelerated drying rate due to the destruction of cell structure. Modified Page model was the fittest model for predicting the FIRD process. Low field nuclear magnetic resonance (LF-NMR) results revealed that T₂ distribution curves of all pretreated samples moved rapidly to the positive x-axis direction, indicating an increase in the rate of water migration. The color of US-FIRD was closer to fresh SUP. BL-FIRD exhibited the highest free polyphenols content (241.28 ± 1.11 mg GAE/100 g DW) and total carotenoids content (129.69 ± 2.49 μg/ g DW), increasing by 45% and 34% respectively compared to the untreated sample.

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.

Terahertz spectroscopy combined with data dimensionality reduction algorithms for quantitative analysis of protein content in soybeans

Protein content in soybean is a key determinant of its nutritional and economic value. The paper investigated the feasibility of terahertz (THz) spectroscopy and dimensionality reduction algorithms for the determination of protein content in soybean. First of all, the THz sample spectrum was data processed by pre-processing or dimensionality reduction algorithms. Secondly, by calibration set, using partial least squares regression (PLSR), genetic algorithms-support vector regression (GA-SVR), grey wolf optimizer-support vector regression (GWO-SVR) and back propagation neural network (BPNN) were respectively used to model protein content determination. Afterwards, the model was validated by the prediction set. Ultimately, the BPNN model combined with linear discriminant analysis (LDA) for related coefficient of prediction set (Rp), root mean square error of prediction set (RMSEP), relative standard deviation (RSD), the time required for the operation was respectively 0.9677, 1.2467%, 3.3664%, and 53.51 s. The experimental results showed that the rapid and accurate quantitative determination of protein in soybean using THz spectroscopy is feasible after a suitable dimensionality reduction algorithm.