Application of fuzzy logic technique for sensory evaluation of high pressure processed mango pulp and litchi juice and its comparison to thermal treatment

Sensory evaluation of microwave-ultrasound treated bottle gourd juice using hybrid features of fuzzy logic and proportional odd modelling approach

In the present investigation, sensory evaluation of bottle gourd juice (BGJ) samples, obtained from microwave-ultrasound based combined treatment was performed. The raw (sample-1) and conventionally treated (sample-2) alongside microwave-ultrasound treated (sample-3) were considered for the assessment of sensory evaluation. An innovative approach of hybrid fuzzy logic and proportional odd modelling (FL-POM) was implemented for the analysis of the sensory scores. The similarity values for the juice samples and their quality attributes were resolved from the results obtained by fuzzy logic. These values were considered as input for hybridization with the POM approach. The assessed coefficients obtained from the results of POM were considered for the ranking of the samples and quality traits. The ranking of the BGJ samples was observed in the order of sample-1 > sample-3 > sample-2, and their related quality attributes ranked in the order color > taste > aroma > mouth feel. The microwave-ultrasound treated BGJ evinced as the best sample in comparison to the raw one.

Application of Fuzzy Logic for Controlling Spray Drying Parameters in Production of Bignay (Antidesma Bunius) Juice Powder

Spray drying is a rapid, continuous, cost-effective, reproducible, and scalable process for reducing the moisture content of a fluid material into a solid powder. To improve this process in juice powder production, automation can be applied to increase efficiency and productivity. Hence, fuzzy logic is used in this study as a control system in the spray-drying process of concentrated liquid bignay juice into juice powder, where the inlet temperature and carrier agent concentrations affecting the properties of the juice powder, such as moisture content and product yield, are considered. The proposed fuzzy system can provide feedback to the control variables, inlet temperature, and carrier agent concentration based on the moisture content and product yield of the juice powder.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

Development of a reduced-calorie high pressure processed sapodilla (Manilkara zapota L.) jam based on rheological, textural, and sensory properties

High pressure technology (400 MPa at 27 ± 1.5 °C for 10 min) was applied for the processing of jam, and target was the reduction (∼47%) of sugar requirement by using a fiber-rich fruit, that is, sapodilla. Different formulations of jam containing various combinations of pectin (0.5 to 5.0%), sugar (45 to 65%), and acid (0.5 to 1) were investigated for textural, rheological, and sensory properties of the pressure-processed jam. The textural parameters mainly hardness (varied 16 to 594 g force) of the jam samples were significantly (P < 0.01) affected by the formulation ingredients viz. sugar and pectin content. Also, an interaction effect (P < 0.01) of sugar and pectin was observed on the jam hardness. The rheological parameters (gel strength, K') also varied (1036 to 2852 Pa) with the change in total soluble solids (TSS) and pectin content. However, the samples having lower TSS content (e.g. 45%) and appropriate pectin content (4.0, 4.5, or 5.0%) were similar (P > 0.05) to the samples having higher TSS content (65%) and corresponding pectin level (0.5, 1.0, or 1.5%) based on the rheological properties. On the other hand, the samples with middle levels of pectin at the corresponding TSS level (45 to 65%) were highest (score of >6 on seven-point hedonic scale) and equally preferred (P > 0.05) by the sensory panelist based on overall acceptability calculated from the scores obtained for sensory attributes viz. color, aroma, sweetness, sourness, texture, and spreadability. Therefore, the jam formulation containing sapodilla pulp as a base material, TSS 45%, pectin 4.5%, and citric acid 0.5% was determined to be the preferred formulation for the production of reduced-calorie, pressure-processed jam based on its gel strength, overall acceptability, and storage stability. PRACTICAL APPLICATION: High-fiber fruit was used for the development of a reduced-calorie high pressure processed jam in this study. The inherited or externally added fiber can favor the reduction in sugar requirement of a food product particularly those processed by high pressure, leading to reduction in calories. The findings of this study can be used for the development of novel HPP products with functional properties.

Analysis on physicochemical and sensory qualities of soymilk prepared by various cultivars: Application of fuzzy logic technique

Soymilk has a complicated system, and its sensory and physicochemical characteristics are influenced by the components of the soybean seeds. Thirty-five soybean cultivars were selected, and correlation analysis and clustering analysis were applied to determine the significant correlations between soybean seed traits and soymilk physiochemical properties. Four sensory quality attributes of soymilk, including taste, odor, appearance, and mouthfeel, were quantified in numerical values and scored based on a weighting criterion which was constructed via the fuzzy logic technique. Soymilk prepared from the soybean seeds with the lower crude protein and the higher crude fat content displayed better sensory qualities. A multivariate linear regression analysis was used to establish a predictive model for overall sensory scores of soymilk, which could be the reference for soybean cultivars selection in soymilk processing. To comprehensively optimize the processing costs, nutritional values, and sensory qualities, a set of detailed attributes of soybean seeds were recommended based on this study: 100-seed weight is higher than 20 g; ash content is less than 5.5 g/100 g; crude fat content is 20 to 24 g/100 g; and crude protein content is as high as possible, while the maximum limit is 40 g/100 g. PRACTICAL APPLICATION: The established predictive model can be a reference for soybean cultivars selection in soymilk processing. Moreover, the soybean composition range that is revealed by this study also provides a recommendation for the soybean breeding.