A new methodology for sensory quality assessment of garlic based on metabolomics and an artificial neural network

Abstract

This study has established a new method for the sensory quality determination of garlic and garlic products on the basis of metabolomics and an artificial neural network. A total of 89 quality indicators were obtained, mainly through the metabolomics analysis using gas chromatography/mass spectrometry (GC/MS) and high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The quality indicator data were standardized and fused at a low level, and then seven representative indicators including the a* (redness) value, and the contents of S-methyl-L-cysteine, 3-vinyl-1,2dithiacyclohex-5-ene, glutamic acid, L-tyrosine, D-fructose and propene were screened by partial least squares discriminant analysis (PLS-DA), analysis of variance (ANOVA) and correlation analysis (CA).Subsequently, the seven representative indicators were employed as the input data, while the sensory scores for the garlic obtained by a traditional sensory evaluation were regarded as the output data. A back propagation artificial neural network (BPANN) model was constructed for predicting the sensory quality of garlic from four different areas in China. The R 2 value of the linear regression equation between the predicted scores and the traditional sensory scores for the garlic was 0.9866, with a mean square error of 0.0034, indicating that the fitting degree was high and that the BPANN model built in this study could predict the sensory quality of garlic accurately. In general, the method developed in this study for the sensory quality determination of garlic and garlic products is rapid, simple and efficient, and can be considered as a potential method for application in quality control in the food industry.

Research on Prediction Model of Hotels’ Development Scale Based on BP Artificial Neural Network Algorithm

“…Affected by the differences of location, economic development level, and tourism development, the scale and spatial distribution of hotels in a certain region are also different [3]. With the development of China's tourism industry, the hotel industry, as the three pillars of the tourism industry, has developed rapidly [4]. According to the data released by the China Hotel Association in January 2018, the overall average annual growth rate of the number of star hotels in the past three years is 6%, while the average annual growth rate of the number of five-star hotels is more than 15%.…”

Metabolic characteristics of taste differences under the soil and hydroponic cultures of sweet potato leaves by using non-targeted metabolomics

“…Metabolomics is a powerful tool for studying the quality traits of crops (Liu et al, 2019;Zhu et al, 2018;Zou et al, 2020). In previous studies, metabolomics has been used successfully for quality study in sweet potato, including anthocyanin accumulation in tuberous roots, metabolic mechanism of flesh color difference, metabolic change during postharvest storage, and metabolic diversity in leaves and roots (Wang et al, 2018;Drapal et al, 2019;He et al, 2020;Ren et al, 2021).…”

“…Metabolomics is the modern instrumental analysis method with high-throughput, high-resolution, and high-sensitivity, to comprehensively study the various features of endogenous metabolites in living organisms (Saito and Matsuda, 2010). For taste quality analysis, metabolomic approaches have yielded new insights into the taste change in the breeding history of tomato (Zhu et al, 2018), the sensory quality assessment of garlic (Liu et al, 2019), exploring the relationships between these substances and tastes of white tea (Yang et al, 2018), and Identification of key taste components in loquat (2020) . Therefore, in this study, we used the untargeted metabolomic analysis of sweet potato leaves under different growing patterns, soil culture and hydroponic culture, for exploring the metabolic foundation of different taste quality in sweet potato leaves.…”

Omics approaches inAlliumresearch: Progress and way ahead

“…Among alliums, metabolic profiling is so far performed in onion bulbs, considering cultivars and cultivation year ( Böttcher et al, 2018 ) ; change in organosulfur metabolites after heat treatment in onion ( Kim et al, 2016 ); differentiation profiling of black and fresh garlics ( Molina-Calle et al, 2017 ) ; low-temperature conditioning of “seed” cloves ( Dufoo-Hurtado et al, 2013 ) ; volatile profiling of onion and garlic after subjecting to different drying methods ( Farag et al, 2017 ) ; metabolic profiling of leaves, bulbs, roots, and basal stems in A. roylei and A. porrum L. ( Abdelrahman et al, 2014 ; Soininen et al, 2014a ; Soininen et al, 2012 ); authentication of the geographic origin of garlic ( Hrbek et al, 2018 ) ; bulb color and flavonoids ( Zhang et al, 2018 ; Abdelrahman et al, 2019 ) ; and metabolic profiling of fresh and stored onion bulbs ( Saviano et al, 2019 ) . Metabolomics, along with the artificial neural network, was used for sensory evaluation of garlic and garlic products by using 89 quality indicators ( Liu et al, 2019 ) . Farag et al (2019) studied the effect of the pickling process on onion metabolome and reported a decrease in secondary metabolites; thus, pickling using brine negatively affects the health benefits of onion.…”