Changes in Quality Attributes During Storage of High-Pressure and Thermally Processed Pineapple Puree

Impact of pulsed light treatment on enzyme inactivation and quality attributes of whole white button mushroom (Agaricus bisporus) and its storage study

Whole white button mushrooms (WWBM) exhibit a limited shelf-life owing to the oxidative enzymatic browning. Inactivation of polyphenol oxidase-PPO and peroxidase-POD in WWBM and its kinetic behavior were studied using pulsed light(PL) treatment (0.13-1.11 J/cm2). The first-order kinetics explained PL-induced enzyme inactivation. Rate constants(k) for PPO and POD were 3.84 and 2.55 cm2/J. FTIR-analysis revealed secondary-structural changes in partially-purified enzyme. PL-treatment retarded browning, retained phenolics and enhanced vitamin D2. PL-treatment at 1.11 J/cm2 rendered WWBM both microbially and enzymatically stable. The PL-treated WWBM's shelf-life at 4, 20, and 37 °C were 5, 3, and 1 day. At 4 °C, browning increased by 6.1 %; firmness decreased by 55.2 %, while PL-treated mushrooms retained 90.6 % phenolics, 78.9 % antioxidant capacity, and 64.2 % D2 after 5 days. Higher activation energy value confirmed phenolics were most sensitive during storage. PL-technology supports UN Sustainable Development Goals by reducing chemical use, lowering carbon-footprints, minimizing pollution, and enhancing shelf-life, promoting sustainable global trade.

Shelf Life Prediction and Kinetics of Quality Changes in Pineapple (Ananas comosus) Varieties at Different Storage Temperatures

Shelf life estimation is an important factor to predict the freshness of fruits. This study aimed to investigate the shelf life and the changes in the physicochemical properties of three different pineapple varieties, namely MD2, Josapine, and Morris stored at 5, 10, and 25 °C. The effect of storage temperature on pineapple changes in total soluble solids, pH, moisture content, firmness, and colour was evaluated for 21 days of the storage period. It was revealed that different storage temperatures have a significant effect on the shelf life and quality of different pineapple varieties. The firmness and moisture content showed high regression coefficients, hence were used for the shelf life prediction of pineapple based on kinetic models. By using first-order kinetics, the coefficient of determination (R2) values for quality changes in pineapples ranged from 0.893 to 0.992. The results also demonstrated that the samples stored at 10 °C had the longest shelf life in relation to the changes in firmness and moisture content of the fruit. The findings indicated that shelf life estimation plays an important role to improve the quality preservation of fresh fruits and vegetables during storage.

Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms

The non-invasive ability of infrared thermal imaging has gained interest in various food classification and recognition tasks. In this work, infrared thermal imaging was used to distinguish different pineapple cultivars, i.e., MD2, Morris, and Josapine, which were subjected to different storage temperatures, i.e., 5, 10, and 25 °C and a relative humidity of 85% to 90%. A total of 14 features from the thermal images were obtained to determine the variation in terms of image parameters among the different pineapple cultivars. Principal component analysis was applied for feature reduction in order to prevent any effect of significant difference between the selected features. Several types of machine learning algorithms were compared, including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and naïve Bayes, to obtain the best performance for the classification of pineapple cultivars. The results showed that support vector machine achieved the best performance from the combination of optimal image parameters with the highest classification rate of 100%. The ability of infrared thermal imaging coupled with machine learning approaches can be potentially used to distinguish pineapple cultivars, which could enhance the grading and sorting processes of the fruit.

Effect of temperature and soluble solid on Bacillus subtilis and Bacillus licheniformis spore inactivation and quality degradation of pineapple juice

Bacillus subtilis and Bacillus licheniformis spores can survive processing temperatures used in the thermal processes of high-acid foods. Therefore, this study investigated the thermal inactivation of B. subtilis and B. licheniformis spores in pineapple juice at different temperatures (85-100°C) and soluble solids (SS, 11-30°Brix). The quality of juices and microbial loads after the thermal treatments during storage at 4 °C for 35 days was then checked. A linear decrease in D-value was observed with increasing temperature of treatment. Furthermore, the D-values determined in pineapple juice were: D_90°C=13.2 ± 0.5 mins, D_95°C = 6.8 ± 0.9 mins and D_100°C = 2.1 ± 1.7 mins for B. subtilis spores, and D_85°C = 16.6 ± 0.4 mins, D_90°C = 7.6 ± 0.5 mins and D_95°C = 3.6 ± 1.5 min, for B. licheniformis. Generally, the susceptibility of the bacteria to soluble solid change was affected by the interaction between temperature, SS and strain. In addition, pasteurization processes of ≥95°C for ≥33.8 mins was needed to ensure a recommended 5-log reduction of B. subtilis spores and limit vitamin C degradation of pineapple juice within three-week of storage at 4 °C.

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.

Gingerols infusion and multi-step process optimization for enhancement of color, sensory and functional profiles of candied mango

Presence of pungent gingerols in ginger oleoresin makes it an ideal natural flavoring candidate for the food industry. The study reports its incorporation for synergistic enhancement of flavor and nutraceutical portfolio of candied mango. The process is systematically optimized at bench-scale for gingerols infusion and subsequent candying treatment in a range of hypo and hypertonic osmotic solutions for critical transport properties. After that, optimization of the drying process and the scale-up study was conducted with a 200 folds increase in the batch size. Collated effects of multistep optimization resulted in 85.6, 76.8, 60.2% retention in β-carotene, total phenolics, Vitamin C, respectively, along with minor color difference and significant improvement in sensory scores over fresh mango. Mass transfer and quality parameters were comparable in both scales, implying excellent repeatability and scalability of the process. Compared with a similar commercial product, substantial improvement in quality characteristics along with 376.7% reduction in overall processing time was achieved.

A Theoretical Analysis for Assessing the Variability of Secondary Model Thermal Inactivation Kinetic Parameters

Traditionally, for the determination of the kinetic parameters of thermal inactivation of a heat labile substance, an appropriate index is selected and its change is measured over time at a series of constant temperatures. The rate of this change is described through an appropriate primary model and a secondary model is applied to assess the impact of temperature. By this approach, the confidence intervals of the estimates of the rate constants are not taken into account. Consequently, the calculated variability of the secondary model parameters can be significantly lower than the actual variability. The aim of this study was to demonstrate the influence of the variability of the primary model parameters in establishing the confidence intervals of the secondary model parameters. Using a Monte Carlo technique and assuming normally distributed DT values (parameter associated with a primary inactivation model), the error propagating on the DTref and z-values (secondary model parameters) was assessed. When DT confidence intervals were broad, the secondary model's parameter variability was appreciably high and could not be adequately estimated through the traditional deterministic approach that does not take into account the variation on the DT values. In such cases, the proposed methodology was essential for realistic estimations.