Valorization of Vegetable Food Waste and By-Products Through Fermentation Processes

Abstract

There is a general interest in finding new ways of valorizing fruit and vegetable processing by-products. With this aim, applications of industrial fermentation to improve nutritional value, or to produce biologically active compounds, have been developed. In this sense, the fermentation of a wide variety of by-products including rice, barley, soya, citrus, and milling by-products has been reported. This minireview gives an overview of recent fermentation-based valorization strategies developed in the last 2 years. To aid the designing of new bioprocesses of industrial interest, this minireview also provides a detailed comparison of the fermentation conditions needed to produce specific bioactive compounds through a simple artificial neural network model. Different applications reported have been focused on increasing the nutritional value of vegetable by-products, while several lactic acid bacteria and Penicillium species have been used to produce high purity lactic acid. Bacteria and fungi like Bacillus subtilis, Rhizopus oligosporus, or Fusarium flocciferum may be used to efficiently produce protein extracts with high biological value and a wide variety of functional carbohydrates and glycosidases have been produced employing Aspergillus, Yarrowia, and Trichoderma species. Fermentative patterns summarized may guide the production of functional ingredients for novel food formulation and the development of low-cost bioprocesses leading to a transition toward a bioeconomy model.

Potential Functional Snacks: Date Fruit Bars Supplemented by Different Species of Lactobacillus spp.

“…Over the last few decades, western nutraceutical and food industries have placed an increasing interest in the formulation of fruit-or vegetable-based fermented foods. These products have found a rapid diffusion on the nutraceutical and food market due to several reasons, primarily nutrition-health approaches, food safety, advantageous sensorial changing, shelf-life prolongation, the facility of preparation, the valorization of unused raw vegetal material, and sustainable development [10][11][12].…”

A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

“…The implementation of an ANN allows describing as much variance as possible, and the cumulative percentage of variance explained by the first five components was 89.2%, which could not be achieved by conventional PCA. Therefore, this kind of mathematical model could be especially suitable to find patterns in biological samples, which may yield sparse and heterogeneous data [ 26 , 27 ]. B. bifidum showed a characteristic glycosidic profile different from the rest of bifidobacteria ( B. adolescentis , B. breve , B. catenulatum , B. dentium , B. longum , B. pseudocatenulatum , B. scardovii ) ( Figure 2 A).…”

“…To compare metabolic profiles of MAGs recovered, glycosidase profiles were expressed as binary data, including presence (value = 1) and absence (value = 0) of glycosidase domains from the same CAZy family in each MAG. According to different criteria, these data were used as input for different machine learning models, powerful pattern-recognition algorithms that allow accurate classification of samples from their biological origin [ 26 , 27 ]. Our machine-learning data analysis strategy involved three main steps: (i) unsupervised distribution of glycosidase profiles of MAGs according to both Bifidobacterium species ( B. adolescentis , B. bifidum , B. breve , B. catenulatum , B. dentium , B. longum , B. pseudocatenulatum , B. scardovii ) and diet type (breastfed, breastfed + GOS, breastfed + GOS + FOS, infant formula-fed, infant formula-fed + FOS, infant formula-fed + GOS, infant formula-fed + GOS + FOS and whole-milk-fed infants as well as fiber-rich diets in adults), (ii) supervised classification of MAGs to accurately elucidate characteristic glycosidic patterns of the main bifidobacteria identified ( B. adolescentis , B. bifidum , B. breve , B. longum , B. pseudocatenulatum ), (iii) association study between glycosidase families found in MAGs.…”

“…Concerning the first step, the unsupervised distribution of MAG glycosidic activities was investigated through artificial neural network-based principal component analysis (PCA). This model was developed to reconstruct experimental data (i.e., presence/absence of functional domains from a specific CAZy family) by the conventional PCA method in combination with artificial neural networks (ANNs) [ 26 , 28 ]. ANNs are among the most popular families of machine learning models that allow modeling complex and highly nonlinear processes.…”

Pembuatan dan Pengujian Pupuk Organik Cair dari Limbah Kulit Buah-buahan dengan Penambahan Bioaktivator EM4 dan Variasi Waktu Fermentasi

“…Sejauh ini sampah tersebut hanya dibuang dan dibiarkan menumpuk tanpa ada pengelolaan yang baik, sehingga dapat menimbulkan bau yang tidak enak dan tentunya akan mengganggu penduduk di sekitarnya serta dapat menimbulkan berbagai dampak kesehatan yang serius. Telah banyak diminati proses untuk memberkan nilai tambah terhadap produk samping pemrosesan buah dan sayur, diantaranya dengan proses fermentasi baik menggunkan bakteri, jamur atau ragi [1]. Salah satu alternatif adalah mengolah sampah-sampah tersebut menjadi pupuk organik cair.…”