Effects of different storage temperatures on the quality and shelf life of Malaysian sweet potato (Ipomoea Batatas L.) varieties

Cyclic variable temperature conditioning induces the rapid sweetening of sweet potato tuberous roots by regulating the sucrose metabolism

This study aimed to investigate the influence of cyclic variable temperature conditioning (CVTC) on the rapid sweetening of sweet potato tuberous roots, as assessed through the analysis of sugar metabolism-related compounds and enzyme activities of tubers during storage. The results showed that CVTC effectively preserved the quality of sweet potato tuberous roots, leading to a significant elevation in soluble solids and soluble sugars. The CVTC group displayed sucrose and fructose levels that were 1.72 and 1.46 times higher, respectively, compared to the control group at the 8 d. Additionally, after storage, the activities of β-amylase, sucrose phosphate synthase (SPS), and sucrose synthase (SS) in the CVTC group were increased by 19.85 %, 60.74 %, and 82.48 %, respectively. Conversely, acid convertase (AI) activity showed inhibition of 64.72 %. In conclusion, implementing CVTC enhanced enzymatic activity in β-amylase, SPS, and SS, facilitating starch degradation and sucrose synthesis, which contributed to the overall improvement in the sweetness of sweet potato tubers.

Three Licorice Extracts' Impact on the Quality of Fresh-Cut Sweet Potato (Ipomoea batatas (L.) Lam) Slices

The quality of fresh-cut produce, particularly sweet potatoes, is crucial for their value. Licorice extract is an optional additive in fresh-cut sweet potatoes. This study examined the impact of three licorice extracts (licorice acid, LA; licorice flavonoids, LF; and licorice polysaccharides, LP) on the quality of fresh-cut sweet potato slices (FCSPSs) for one week of storage. After one week of storage, the extracts showed varying effects on FCSPSs. LA and LF treatments reduced the area proportion of browning (APB), while LP treatments increased APB and decreased L* values. Antioxidant experiments revealed that LP treatments increased PPO and POD activity while reducing SOD activity. The concentrations of the three licorice extracts showed a strong negative correlation with SOD activity. In conclusion, LP harmed the appearance and antioxidant qualities of FCSPSs. LA and LF may be suitable additive components for FCSPSs, and 30 mg/mL LA and LF treatments were found to maintain the appearance and texture quality of FCSPSs during storage. Therefore, careful consideration should be given when using LP as a food additive for FCSPSs.

Effect of potato peel on the determination of soluble solid content by visible near-infrared spectroscopy and model optimization

The quantitative determination of the soluble solid content (SSC) of potatoes using NIR spectroscopy is useful for predicting the internal and external quality of potato products, especially fried products. In this study, the effect of peel on the partial least squares regression (PLSR) quantitative prediction of potato SSC was investigated by transmission and reflection. The results show that the variable sorting for normalization (VSN) pre-processing method improved model accuracy. Additive multiplicative scattering effects and intensity drift interference of the peels were reduced. The model accuracy reached a correlation coefficient of prediction (RP) of 0.85. The selection algorithm using variable combination population analysis and iterative retention of information variables (VCPA-IRIV) demonstrated that peel increases unnecessary information. When the effect of irrelevant variables was reduced, the results reached RP = 0.88 and the root mean square error of prediction (RMSEP) = 0.25 in the transmission mode was close to that of the full-wavelength peeled PLSR model (RP = 0.89 and RMSEP = 0.25). This indicates that the use of the combined algorithm (VSN-VCPA-IRIV) reduces the effect of the peel and enables samples with a peel to still be predicted accurately in the full-wavelength model. It also improves detection efficiency through the extraction of the necessary variables and optimizes the stability and accuracy of the model.

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.

Kibr G. Effect of blending ratio of wheat, orange fleshed sweet potato and haricot bean flour on proximate compositions, β-carotene, physicochemical properties and sensory acceptability of biscuits'

Background: Protein-energy malnutrition and vitamin A deficiency (VAD) are the most important public health issues, and a food-based strategy is crucial to combat those health problems among the vulnerable group of people. Methods: Composite biscuits were made with 100:0:0, 90:5:5, 80:10:10, 70:15:15, 60:20:20, and 50:25:25 percent wheat, haricot bean, and orange-fleshed sweet potato (OFSP) flours.Standard methods were used to evaluate the proximate compositions, β-carotene, physical properties, functional properties, and sensory acceptability. A one-way analysis of variance model was used to statistically evaluate the data using the statistical analysis system software package, version 9.0 standard methods. Results: The results showed that partially replacing wheat with haricot beans and OFSP increased the β-carotene and proximate compositions significantly. When wheat was replaced with haricot beans and OFSP, the physical characteristics of the biscuits did not vary significantly from those of biscuits made entirely of wheat flour. Sensory acceptability (appearance, color, flavor, taste and overall acceptability) was higher in the composite biscuits with up to 40% wheat substitution than in the 100% wheat flour biscuits. Conclusion: Based on the findings of this report, replacing wheat with OFSP and haricot beans in biscuit formulation appears to be promising in improving nutritional quality, sensory acceptability, and beta carotene. It is proposed that these products can mitigate food insecurity and deficiency of vitamin A.