The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is a growing interest in the use of non-contact techniques to automate this process. Machine learning techniques, such as artificial neural networks, support vector machines (SVMs), decision trees, and K-nearest neighbor algorithms, have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, the combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a fivefold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of increased complexity. INDEX TERMS Cape gooseberry, artificial neural networks, support vector machines, decision trees, K-nearest neighbors, color spaces, PCA, multiclass confusion matrix.

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Food packaging materials are commonly derived from petroleum that increases global contamination; this raises the interest to evaluate raw material from renewable sources such as whey protein for the development of packaging materials, especially to produce active films. This research aimed to evaluate whey protein-based film properties when natamycin, nanoemulsioned α-tocopherol, or both were added. An oil-in-water (O/W) nanoemulsion of antioxidant (α-tocopherol) was prepared by microfluidization technique. Four films were prepared with different levels of natamycin and nanoemulsified α-tocopherol and were characterized in terms of physicochemical, mechanical, optical-properties, water vapor barrier, FTIR, microstructure, antioxidant and antimicrobial activity. The natamycin, nanoemulsified α-tocopherol, or both did not modify the moisture content of the films. Moreover lead to a significant reduction of tensile strength and elastic modulus, while presenting growth in the elongation at break. Film opacity, the total color difference, the UV-Vis light barrier, and the water vapor permeability values increased when compounds were incorporated into the film. The microstructure studies showed uniformly distributed porosity throughout the films. The addition of nanoemulsioned α-tocopherol into whey protein-based films provoked antioxidant activity and the addition of natamycin produced films with effectivity against C. albicans, P. chrysogenum, and S. cerevisiae, allowing develop a material appropriate for use as active food packaging.

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time.

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