Characterization of Chinese white-flesh peach cultivars based on principle component and cluster analysis

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high ("H") and low ("L") level of saccharification a chosen carrier (potato maltodextrin).

Effects of green manure planting mode on the quality of Korla fragrant pears (Pyrus sinkiangensis Yu)

In this study, a three-year experiment on the fragrant pear orchard was conducted to investigate the effects of different varieties of green manure on the Korla fragrant pear fruit quality, with a view to finding a suitable green manure planting mode for Korla fragrant pear orchard. Green manures were planted in spaces among rows of pear trees, and then smashed and pressed into the soil as fertilisers by the agricultural machinery equipment in their full bloom period. In the experiment, four planting modes of green manure had been set for comparison: SA: Leguminosae green manures alfalfa (Medicago sativa L.), SP: Poaceae green manures oats (Avena sativa L.), ST: Cruciferae green manures oilseed rape (Brassica napus L.), and S: orchard authigenic green manures (Chenopodium album L., Mulgedium tataricum (L) DC., and Phragmites australis (Cav.) Trin. ex Steud.). Apart from that, eleven fruit quality indicators were analyzed to evaluating the effects of different green manure planting mode on the quality of fragrant pear. According to analysis of variance (ANOVA) results, there were significant differences among four planting modes in terms of nine fruit quality indicators (P<0.05). In addition, the correlation analysis (CA) results revealed that there were different degrees of correlations among quality indicators. On this basis, repeated information among indicators was eliminated by principal component analysis (PCA), thus simplifying and recombining the three principal components. All in all, these three principal components reflect appearance traits, internal nutritive value and taste of fruits, respectively. Specifically, SA significantly improved the internal quality and nutritive value of fruits, SP improved the physical traits of fruits, and ST significantly improved the taste of fruits. Based on the PCA results, a comprehensive evaluation model of fruit quality was constructed. The are comprehensive fruit quality scores:SA>SP>ST>S.

Pattern Recognition of Varieties of Peach Fruit and Pulp from Their Volatile Components and Metabolic Profile Using HS-SPME-GC/MS Combined with Multivariable Statistical Analysis

A fruit's aroma profile, composed of a complex mixture of volatile organic compounds, is among the core attributes related to the overall taste and consumer preference. Prunus persica L. is a preferred summer fruit with a distinct, favorable olfactory characteristic. The volatile compositions of both peach fruits and fruit pulps from eight peach cultivars (four native and four introduced) was investigated to compare their composition and assess flavor-contributing compounds. In total, 65 compounds were profiled after a HS-SPME-GC-MS analysis: 16 esters, 14 aldehydes, 5 alcohols, 7 hydrocarbons, 7 ketones, 8 acids, and 8 terpenes. The most common compounds were esters, acids, and aldehydes. Although the same compounds were identified in both fruit and pulp, their %TIC (total ion current) differed in favor of the whole fruit. Following the metabolic profiling of the whole fruit and fruit pulp, a total of 44 compounds were identified from the studied varieties. Among them, amino acids, organic acids, sugar alcohols, saccharides, fatty acids, and phenolic acids were identified as existing groups. According to the provided principal component analysis (PCA) and hierarchical cluster analysis (HCA), the relative %TIC of the identified volatile compounds fluctuated depending on the studied cultivar. No differences were visible in the PCA biplots, which suggested that the polar and lipid metabolites do not provide significant variations when considering different parts of the fruit, contrary to the volatile compounds. The obtained results could successfully be applied in the metabolic chemotaxonomy of peaches and the differentiation of the metabolites present in different parts of the peach.

Physicochemical Characteristics, Antioxidant Activities, and Aroma Compound Analysis of Seven Peach Cultivars (Prunus persica L. Batsch) in Shihezi, Xinjiang

Peaches are tasty and juicy, with a unique flavor. The flavors of peaches always vary with cultivars. To investigate the physicochemical and aroma characteristics of peaches, the sugars, organic acids, total flavonoids, phenols, antioxidant activities, and aroma compounds of seven peach cultivars in Xinjiang were determined using high-performance liquid chromatography (HPLC) and headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME–GC–MS). The results showed that sucrose (59.83 to 87.34%), malic acid (32.41 to 59.14%), and chlorogenic acid (10.43 to 45.50%) were the dominant sugar, organic acid, and phenolic compound in peaches, respectively. The antioxidant activity varied between 147.81 and 394.55 μmol TEs/100 g. The analysis of the aroma structure of peaches found that the volatile composition of peaches was relatively consistent, though the concentration of total aroma and certain separate compounds were different between cultivars. Meanwhile, the aroma fingerprint of the peaches consisted of hexyl acetate, cis-3-hexenyl acetate, γ-decalactone, n-hexanal, 2-hexenal, nonanal, decanal benzaldehyde and 6-pentylpyran-2-one, providing a clear green, sweet, floral, and fruity odor. These results provide complete information on the physicochemical properties, functional ingredients and aroma of the peaches.

Analysis of the Fruit Quality of Pear (Pyrus spp.) Using Widely Targeted Metabolomics

Pear is a kind of common temperate fruit, whose metabolite composition that contributes to the difference in fruit quality is unclear. This study identified and quantified the metabolites using a widely targeted LC-MS/MS approach in three pear species, including Pyrus bretschneideri (PB), Pyrus usssuriensis (PU) and Pyrus pyrifolia (PP). A total of 493 metabolites were identified, consisting of 68 carbohydrates, 47 organic acids, 50 polyphenols, 21 amino acids, 20 vitamins, etc. The results of PCA and OPLS-DA demonstrated that the metabolite compositions differed distinctly with cultivar variability. Our results also involved some metabolic pathways that may link to the fruit quality based on KEGG pathway analysis, the pathway of phenylalanine metabolism revealed significant differences between PB and PP (p < 0.05). Furthermore, the study selected D-xylose, formononetin, procyanidin A1 and β-nicotinamide mononucleotide as the major differentially expressed metabolites in the three species. The present study can open new avenues for explaining the differences in fruit quality of the major commercial pear cultivars in China.

Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits

This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit weight, juice volume, and sphericity percent of fresh peach fruit. Multiple regression analysis (MLR) and an artificial neural network (ANN) were employed. An ANN model was developed with six inputs and 15 neurons in the first hidden layer for the prediction of six chemical composition parameters. The results confirmed that the ANN model R² = 974–0.998 outperformed the MLR models R² = 0.473–0.840 using testing dataset. Moreover, sensitivity analysis revealed that the juice volume was the most dominating parameter for the prediction of titratable acidity, TSS/titratable acidity and vitamin C with corresponding contribution values of 39.97%, 50.40%, and 33.08%, respectively. In addition, sphericity percent contributed by 23.70% to anthocyanin and by 24.08% to total carotenoids. Furthermore, hue on TSS prediction was the highest compared with the other parameters, with a contribution percentage of 20.86%. Chroma contributed by different values to all variables in the range of 5.29% to 19.39%. Furthermore, fruit weight contributed by different values to all variables in the range of 16.67% to 23.48%. The ANN prediction method denotes a promising methodology to estimate targeted chemical composition levels of fresh peach fruits. The information of peach quality reported in this investigation can be used as a baseline for understanding and further examining peach fruit quality.

Application of a multilayer perceptron artificial neural network for identification of peach cultivars based on physical characteristics

In the fresh fruit industry, identification of fruit cultivars and fruit quality is of vital importance. In the current study, nine peach cultivars (Dixon, Early Grande, Flordaprince, Flordastar, Flordaglo, Florda 834, TropicSnow, Desertred, and Swelling) were evaluated for differences in skin color, firmness, and size. Additionally, a multilayer perceptron (MLP) artificial neural network was applied for identification of the cultivars according to these attributes. The MLP was trained with an input layer including six input nodes, a single hidden layer with six hidden nodes, and an output layer with nine output nodes. A hyperbolic tangent activation function was used in the hidden layer and the cross entropy error was given because the softmax activation function was functional to the output layer. Results showed that the cross entropy error was 0.165. The peach identification process was significantly affected by the following variables in order of contribution (normalized importance): polar diameter (100%), L∗ (89.0), b∗ (88.0%), a∗ (78.5%), firmness (71.3%), and cross diameter (37.5.3%). The MLP was found to be a viable method of peach cultivar identification and classification because few identifying attributes were required and an overall classification accuracy of 100% was achieved in the testing phase. Measurements and quantitative discrimination of peach properties are provided in this research; these data may help enhance the processing efficiency and quality of processed peaches.

Chemometrics-based aroma profiling for revealing origin, roasting indices, and brewing method in coffee seeds and its commercial blends in the Middle East

Coffee is among the most consumed beverages worldwide. The present study reports on the aroma composition associated with coffee seeds brewing. Aroma of authentic coffee specimens of Coffea arabica and C. robusta alongside with typical products consumed in the Middle East were analyzed using HS-SPME coupled with GC-MS. In addition, multivariate data analysis (MVA) was employed. Results revealed for 102 volatiles with a distinct aroma profile between the different brewing methods. Infusion demonstrated higher esters level, while decoction and maceration were more abundant in sesquiterpenes and terpene alcohols, respectively. Besides, heat-induced products, i.e., 4-vinyl guaiacol was identified as potential roasting index in instant coffee and roasted C. robusta brews. Blending with cardamom further masked the smoky odor of such compounds by its fragrant terpinyl acetate. This study provides the first report on the chemical sensory attributes of Middle Eastern coffee blends and further reveal for the impact of brewing, roasting on its aroma composition.

Metabolomics reveals impact of seven functional foods on metabolic pathways in a gut microbiota model

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Metabolomics was employed to assess 7 functional foods impact on gut microbiota.

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Insights regarding how functional foods alter gut metabolic pathways is presented.

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Increased GABA production was observed in polyphenol rich functional food.

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Purine alkaloids served as direct substrate in microbiota metabolism.

Functional food defined as dietary supplements that in addition to their nutritional values, can beneficially modulate body functions becomes more and more popular but the reaction of the intestinal microbiota to it is largely unknown. In order to analyse the impact of functional food on the microbiota itself it is necessary to focus on the physiology of the microbiota, which can be assessed in a whole by untargeted metabolomics. Obtaining a detailed description of the gut microbiota reaction to food ingredients can be a key to understand how these organisms regulate and bioprocess many of these food components. Extracts prepared from seven chief functional foods, namely green tea, black tea, Opuntia ficus-indica (prickly pear, cactus pear), black coffee, green coffee, pomegranate, and sumac were administered to a gut consortium culture encompassing 8 microbes which are resembling, to a large extent, the metabolic activities found in the human gut. Samples were harvested at 0.5 and 24 h post addition of functional food extract and from blank culture in parallel and analysed for its metabolites composition using gas chromatography coupled to mass spectrometry detection (GC-MS). A total of 131 metabolites were identified belonging to organic acids, alcohols, amino acids, fatty acids, inorganic compounds, nitrogenous compounds, nucleic acids, phenolics, steroids and sugars, with amino acids as the most abundant class in cultures. Considering the complexity of such datasets, multivariate data analyses were employed to classify samples and investigate how functional foods influence gut microbiota metabolisms. Results from this study provided a first insights regarding how functional foods alter gut metabolism through either induction or inhibition of certain metabolic pathways, i.e. GABA production in the presence of higher acidity induced by functional food metabolites such as polyphenols. Likewise, functional food metabolites i.e., purine alkaloids acted themselves as direct substrate in microbiota metabolism.