Nutritional and Physicochemical Quality of Vacuum-Fried Mango Chips Is Affected by Ripening Stage, Frying Temperature, and Time

Characteristics of wood apple (Limonia acidissima L.) and soybean powder jelly for emergency food alternatives

The substitution of wood apple juice and soybean powder in the seaweed jelly product can be used as an alternative to emergency supplementary feeding (ESF) for children under five years of age, which contains high protein, fiber, and calories. This study aimed to determine the effect of adding wood apple juice and soybean powder to the nutrition content, vitamin C, zinc, magnesium, total phenol, antioxidant activity, acceptability, and shelf-life of seaweed jelly products. This study was an experimental study with a completely randomized design with two treatment factors, which consisted of making seaweed jelly products with three different ratios of wood apple juice and soybean powder, 60:40 (F1), 50:50 (F2), and 40:60 (F3), dried at 40 °C (T1) and 50 °C (T2). Macronutrients were determined using proximate analysis. The total phenol and vitamin C were measured using Folin-ciocalteu reagent and UV-Vis spectrophotometry. Antioxidant activity was analyzed by 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH). The contents of zinc and magnesium were evaluated through Atomic Absorption Spectrophotometry (AAS). Estimation of shelf life was determined with Accelerated Shelf-Life Test (ASLT) method and Arrhenius equation model. The best formula based on proximate analysis was F3, which contained 361.98 kcal of energy and 33.79 g of protein. The best formula (F1) dried at 40 °C; contains 56.28 mg/100 g vitamin C; zinc was 1.55 mg/100 g; magnesium was 79.25 mg/100 g; antioxidant activity (IC50) was 88.39 μg/mL; and total phenol was 8.59 mg GAE/g. The quality attributes of the best formula show the potential of the jelly as an emergency food despite its short shelf-life.

Mitigation of Acrylamide in Potato Chips by Pre-drying and Pulsed Electric Field Treatment

The object of this work was to study the effects of preliminary vacuum drying (VD), pulsed electric field (PEF) treatment, frying temperature on color, oil uptake, and acrylamide (AA) content in fried potato chips. The results of this study indicated that an increase of frying temperature from 120 to 180°C led to a decrease of frying time of around 70% for untreated and PEF pre-treated samples. The color value of L* and a* decreased with the increase of frying temperature, and those values of the sample pre-treated by PEF were significantly higher compared to those obtained from untreated samples. The PEF pre-treatment promoted the reduction of oil content of fried samples by up to 17.6, 14.2, and 16% compared with untreated samples at the frying temperatures of 120, 150, and 180°C, respectively. Higher efficiency was observed by applying the preliminary VD in the case of the frying temperature of 150°C. Furthermore, it was revealed that PEF pre-treatment and preliminary VD application lead to a synergetic effect on the reduction of AA content in potato chips. For example, with the initial moisture ratio of 0.5, pre-dried by VD and pre-treated by PEF, the AA content was noticeably decreased from 2,220 to 311 μg/kg compared to untreated and undehydrated samples at the frying temperature of 150°C. Our findings provide reference for a new pre-treatment to mitigate AA formation and to improve the quality of potato chips.

Application of Machine Vision System in Food Detection

Food processing technology is an important part of modern life globally and will undoubtedly play an increasingly significant role in future development of industry. Food quality and safety are societal concerns, and food health is one of the most important aspects of food processing. However, ensuring food quality and safety is a complex process that necessitates huge investments in labor. Currently, machine vision system based image analysis is widely used in the food industry to monitor food quality, greatly assisting researchers and industry in improving food inspection efficiency. Meanwhile, the use of deep learning in machine vision has significantly improved food identification intelligence. This paper reviews the application of machine vision in food detection from the hardware and software of machine vision systems, introduces the current state of research on various forms of machine vision, and provides an outlook on the challenges that machine vision system faces.

Nutrient Content, Organoleptic Quality, and Shelf Life of Sagon Substitute From Lindur (Bruguiera gymnorrhiza L.) and Soybean Flour (Glycine max L.), as an Alternative Emergency Food

Lindur (Bruguiera gymnorrhiza L.) fruit as a mangrove species has not been widely developed. However, the combination of lindur fruit functional food with soybean flour has high carbohydrates and protein potential to serve as an additional food product in emergency conditions. Therefore, this study aims to evaluate the nutritional content, dietary fiber, organoleptic quality, and sagon shelf-life estimation of lindur and soybean flour, in accordance with emergency food quality requirements. The shelf life was determined using the Accelerated Shelf-life Testing (ASLT) method with the Arrhenius equation, where the sample was stored in an incubator at a temperature of 27, 37, as well as 47°C for 30 days, and the water content, peroxide number, and free fatty acid content were tested every 5 days. In addition, formulation P1 comprising 45% lindur flour and 35% soybean flour was discovered to be the best formulation, with a composition of 251.5 kcal/bar of energy, 6.3 g of fat, 4.4 g of protein, 30 g of carbohydrates, 15.79% dietary fiber, 1.84% ash content, and 4.03% water content. Therefore, the nutritional content of sagon substitution for lindur flour is in accordance with the emergency food quality requirements, except for the protein content. Also, the P2 sagon sample’s shelf life was estimated to be 37 days in polypropylene plastic packaging at a room temperature of 27°C.

Study on the Relations between Hyperspectral Images of Bananas (Musa spp.) from Different Countries, Their Compositional Traits and Growing Conditions

Bananas are some of the most popular fruits around the world. However, there is limited research that explores hyperspectral imaging of bananas and its relationship with the chemical composition and growing conditions. In the study, the relations that exist between the visible near-infrared hyperspectral reflectance imaging data in the 400-1000 nm range of the bananas collected from different countries, the compositional traits and local growing conditions (altitude, temperature and rainfall) and production management (organic/conventional) were explored. The main compositional traits included moisture, starch, dietary fibre, protein, carotene content and the CIE L*a*b* colour values were also determined. The principal component analysis showed the preliminary separation of bananas from different geographical origins and production systems. The compositional and spectral data revealed positively and negatively moderate correlations (r around ±0.50, p < 0.05) between the carotene, starch content, and colour values (a*, b*) on the one hand and the wavelength ranges 405-525 nm, 615-645 nm, 885-985 nm on the other hand. Since the variation in composition and colour values were related to rainfall and temperature, the spectral information is likely also influenced by the growing conditions. The results could be useful to the industry for the improvement of banana quality and traceability.