Naveen Kumar Mahanti

Infrared (IR) radiation, a versatile and efficient heating method, has garnered significant attention in the food and oil processing industries because of its unique heat transfer mechanism and broad range of applications. IR is applied in drying, peeling, decontamination, enzyme inactivation, and roasting, underscoring its potential for energy‐efficient, high‐quality processing. Its application to oil‐bearing materials improves moisture removal rates and supports the preservation of critical quality parameters. The multifaceted impact of IR on vegetable oil processing includes enhanced extraction yields, reduced processing times, and lower energy consumption. It also affects fatty acid (FA) composition, contributing to the stability and preservation of essential FAs. Changes in lipid profiles and minor bioactive compounds have been observed under various IR treatments, highlighting the importance of optimizing process parameters to maintain nutritional and functional properties. Overall, IR processing represents a promising alternative for sustainable and efficient oil processing, warranting further optimization for industrial scalability.

The use of calcium carbide (CaC ₂ ) for the artificial ripening of fruits is a common practice in some regions, despite being banned in many countries due to health concerns. CaC ₂ reacts with moisture to produce acetylene gas, which accelerates the ripening process. However, the presence of acetylene gas and its byproducts in CaC ₂ ‐ripened fruits can pose risks to human health. Therefore, there is a need for non‐destructive techniques to detect CaC ₂ ‐induced ripening and ensure the safety and quality of fruits. This review explores the scope and application of non‐destructive techniques for the detection of CaC ₂ ‐induced ripening of fruits. Various spectroscopic methods, including near‐infrared spectroscopy, Raman spectroscopy, and hyperspectral imaging, have been investigated for their potential to identify fruits ripened using CaC ₂ . Additionally, chemical tests and sensor‐based approaches have been explored to detect residues of CaC ₂ and its byproducts in fruits. The review examines the principles behind these techniques and discusses their advantages, limitations, and practical applications. Non‐destructive techniques are promising for detecting CaC ₂ ‐induced ripening of fruits. Spectroscopic methods allow rapid identification of characteristic signatures in ripened fruits, whereas chemical tests and sensor‐based approaches detect CaC ₂ residues. These tools ensure fruit safety and authenticity, protecting consumer health. Further research is needed to improve the sensitivity and reliability of these methods for wider use in the food industry.

Foxtail millet is a type of minor millet that has nutritional‐rich characteristics. The bran layer in millets contains a considerable amount of antinutritional properties, and grain/flour/product spoilage occurs due to fat in the bran layer. Therefore, the removal of bran from millets can enhance their shelf life. Bran removal from foxtail millet is challenging because of its tiny size. Hence, the study targeted optimizing polishing process parameters for foxtail millet. The effect of process variables such as grain moisture content (8%–16% w.b.), emery roller speed (800–2400 rpm), and polishing time (1–8 min) on polishing characteristics (degree of polishing, head yield, middling yield, and broken content) and proximate composition (protein, fat, crude fiber, ash, and carbohydrate contents) of foxtail millets was studied using the central composite rotatable design (CCRD). Emery roller speed and polishing time significantly affected all responses (p < 0.01). In contrast, grain moisture content had a significant effect only on the degree of polishing (p < 0.05), head, and middling yields (p < 0.01). A numerical optimization technique was adopted to optimize the process parameters of the polisher. The optimum operating conditions were 10% grain moisture content, 1650 rpm of emery roller speed, and 2 min of polishing time. A validation study conducted at optimized conditions revealed no significant difference between the predicted and actual values of responses and considerably less broken content (9.26%). The results of the present study indicate that machinery modified and processes optimized during the study are fairly accepted and can be used to upscale the machine for industrial use.

Excess nitrogen fertilizer use leads to vegetables with high amounts of nitrate content. Consumption of vegetables with high amounts of nitrate is carcinogenic to human beings. In this study, fenugreek plants were grown under varying nitrogen fertilizer doses (0, 50, 100, 150, 200, 250, 300, 350 and 400 kg N/ha). A Vis-NIR hyperspectral imaging (HIS) camera captured images of fenugreek leaves within the 398–1,003 nm spectral range. The spectral data were pre-processed using different pre-processing techniques before the model development. Partial least-squares regression (PLSR) models were constructed with complete spectral data and selected wavelengths. The performance of the PLSR model decreased with pre-processed spectral data, and there was no significant difference compared to the model constructed with raw spectral data (R ² _CV = 0.915, SECV = 591.933, slope = 0.518 and RPDCV = 1.421). The wavelengths 411, 435, 466, 558, 669, and 720 nm were selected as feature wavelengths for representing nitrate content in fenugreek leaves. The performance of the PLSR model constructed with feature wavelengths (SECV = 648.672; RPDCV = 1.482; R ² _CV = 0.869) was non-significant compared with the model developed with raw complete spectral data (SECV = 591.933; R ² _CV = 0.915 and RPDCV = 1.421). Using the complete raw spectral data, the spatial distribution images of nitrate content in fenugreek leaves indicated that the nitrate content was concentrated near and along the midrib up to the apex. The overall results obtained in the present study suggest that VIS-NIR HSI, along with suitable chemometric techniques, can be used for rapid assessment of nitrate content in fenugreek leaves.

The quality of vegetables depends upon the preharvest and postharvest practices. The preharvest practices include type, amount, form of fertilizers, harvesting period, growing season, and conditions that influence the yield of the crop and affect its chemical, biochemical, and mechanical properties. Moreover, postharvest practices such as storage conditions and processing methods also affect the quality of vegetables. Therefore, the present study was conducted to study the effect of different doses of nitrogen fertilizer (0–400 kg·N/ha) and storage conditions on the quality attributes of spinach leaves such as respiration rate, physiological loss in weight (PLW), chromatic properties, chlorophyll, puncture strength, TSS, pH, and nitrate during the storage studies. The respiration rate of leaves decreased with an increase in fertilizer dose. The a^∗and ΔE values of spinach leaves significantly (p < 0.01) increased with an increase in storage temperature (T) and storage time (t) and decreased significantly (p < 0.01) with an increase in fertilizer dosage (F). The puncture strength (PS) of leaves significantly (p < 0.01) decreased with an increase in F, T, and t. The T, t, and their interaction had a significant (p < 0.05) negative effect on the chlorophyll content of spinach leaves for all treatments. However, the T, t, and their interaction had a significant (p < 0.01) positive effect on pH and PLW and a negative effect on the nitrate content. The colour change, nitrate, and chlorophyll degradation were higher with storage time for leaves which have a higher amount of nitrate content initially. The TSS, pH, PLW, and mechanical properties degradation were higher with storage time for leaves that initially had a lower amount of nitrate content. Therefore, application of the recommended dosage (as per the soil nutrient status) of nitrogen fertilizer can produce leaves with minimum nitrate content, and storage of leaves under refrigerated conditions can enhance the shelf life.

The vacuum impregnation (VI) process parameters (vacuum pressure = 20–60 kPa; VI temperature = 35–55°C; concentration of the sucrose solution = 40–60 °Brix; and vacuum process time = 8–24 min) for pineapple rings were optimized based on the moisture content (MC), water loss (WL), solids gain (SG), yellowness index (YI), and total soluble solids (TSS) content of pineapple rings using response surface methodology (RSM). A relationship was developed between the process and response variables using RSM and artificial neural network (ANN) techniques. The effectiveness of VI was evaluated by comparing it with the osmotic dehydration (OD) technique. The optimum condition was found to be 31.782 kPa vacuum pressure, 50.441°C solution temperature, and 60 °Brix sucrose concentration for 20.068 min to attain maximum TSS, YI, SG, and WL, and minimum MC of pineapple rings. The R ² values of RSM models for all variables varied between 0.70 and 0.91, whereas mean square error values varied between 0.76 and 71.58 and for ANN models varied between 0.87–0.93 and 0.53–193.78, respectively. Scanning electron micrographs (SEM) revealed that parenchymal cell rupture was less in VI than in OD. The VI pineapple rings exhibited more pores and high SG, as compared to OD, due to the pressure impregnation. Spectroscopic analysis affirmed that the stretching vibrations of intermolecular and intramolecular interactions were significant in VI as against OD. The VI reduced the drying time by 35% compared to OD, with the highest overall acceptability score and lower microbial load during storage. Practical Application: Pineapple is a perishable fruit, which necessitates processing for extended shelf life. This study highlights the potential of the vacuum impregnation process as a promising alternative to conventional preservation methods such as osmotic dehydration for pineapples.

Texture is an important sensory attribute that drives consumer acceptance of any food material. In recent times consumers' demand for high‐quality food urges food industries to provide food with consistent textural properties. However, texture measurement not just requires a trained sensory panel but also a considerable amount of time and effort. On the flip side, human observation could be subjective hence repeatability of the result may not be ensured and/or relied on. Contrary to that, objective methods for texture measurement are reliable and consistent, but are not suitable for in‐line application and also destructive in nature. The mentioned crisis has made industries opt for nondestructive texture analysis techniques. In the past decade, considerable research has been carried out on nondestructive texture analysis methods such as micro‐deformation, and acoustic and optical techniques, showing feasibility for in‐line applications. The current review focuses on the working principles and most recent applications of nondestructive techniques for texture analysis of food products. Moreover, a detailed review of contact and noncontact‐type texture measurement has been presented in this article. The literature survey is concluded with future research aspects and challenges involved in the commercialization of the nondestructive texture analysis techniques.

Summary: Thermal processing of tender jackfruit is a promising approach to enhance its shelf life and thereby retain the health benefits associated with its inherent antioxidants and phytochemicals. This research looked at the prospective and suitability of near‐infrared reflectance (NIR) spectroscopy (900–1700 nm) to assess the biochemical properties namely ascorbic acid (AA), total flavonoid content (TFC), and total phenolic content (TPC) of thermally treated (pasteurisation and sterilisation) tender jackfruit stored under different preservatives (2% brine, 0.1% KMS, and 0.3% citric acid) for a period of 5 months. The partial least square regression (PLSR) models were developed by using both raw as well as pre‐processed spectra. The pre‐processing of spectral data diminished the prediction accuracy of models. The models developed with raw spectral data reported best prediction accuracies for AA ( = 0.628; RMSE_cv = 0.416 and RPD_cv = 1.763), TFC ( = 0.915; RMSE_cv = 1.909 and RPD_cv = 3.719), and TPC ( = 0.882; RMSE_cv = 0.032 and RPD_cv = 3.013) as compared to models developed with pre‐processed spectral data. However, the lower prediction accuracies were observed in standard normal variate (SNV) pre‐processed data for AA ( = 0.449; RMSE_cv = 0.51), TFC ( = 0.500; RMSE_cv = 4.628), and TPC ( = 0.396; RMSE_cv = 0.072). The standard error (SE_P) values of AA, TPC and TFC were almost 1.5–2 times the standard error of laboratory (SE_L); this implies the developed models have excellent performance. The results obtained in the present study affirmed that NIR spectroscopy in conjunction with appropriate chemometric techniques can be used for the rapid assessment of biochemical properties of thermal processed and canned tender jackfruit during transportation and storage.