Change in the color of heat‐treated, vacuum‐packed broccoli stems and florets during storage: effects of process conditions and modeling by an artificial neural network

Abstract

The greenness of both stems and florets during storage can be better preserved at higher temperatures (99 °C) and short times. The simulation results revealed that the ANN method could be used as an effective tool for predicting and analyzing the color values of heat-treated broccoli. © 2018 Society of Chemical Industry.

Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine

“…Increasing research on NIR spectroscopy coupled with machine learning (ML) modeling has gained significant interest in the food industry [89][90][91][92]. A branch of artificial intelligence (AI), ML refers to a computer-based system that may be trained to find patterns among datasets, which can then be used to classify or predict various parameters and improve its performance by feeding new data [90].…”

“…Machine learning may be divided into supervised and unsupervised algorithms, of particular interest are the supervised groups, which are further sub-classified into (i) classification or pattern recognition, which is used to categorize samples into different groups, and (ii) regression algorithms, which are used to predict specific attributes or parameters such as chemometrics or intensities of sensory attributes [90,93]. While several different classification and regression algorithms exist, the use of artificial neural network (ANN) modeling has been widely researched due to its ability to find complex and non-linear relationships between the inputs and the outputs [92,93]. ANNs are ML models based on the function of the human brain [94][95][96].…”

Modeling by artificial neural networks of silver carp (Hypophthalmichthys molitrixi) with sous vide processing on the effects of storage and processing temperatures on the microbiological status

“…As there are many modeling and optimization techniques applied in food technology [7], artificial neural networks (ANNs) have gained special attention due to their ability in modeling complex processes where there is a nonlinear relationship between the dependent and independent variables [8]. Furthermore, this modeling technique has successfully been applied in modeling and optimization of food processes [9][10][11] and microbial analysis [12][13][14][15].…”

Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

“…Several chemometric techniques have been used to analyze spectral data, including partial least squares (PLS) regression, principal component analysis (PCA), and artificial neural networks (ANN), to name a few [ 41 ]. Of these techniques, ANNs have increased in popularity as classification, prediction, and clustering tools, particularly since they can better interpret the non-linear patterns of spectral data [ 51 , 52 , 53 , 54 ]. Machine learning (ML) modeling based on ANN can be trained from a set of given data known as ‘inputs’ or independent variables and form complex, non-linear relationships with these inputs and the ‘targets’ or dependent variables [ 54 ].…”

“…Of these techniques, ANNs have increased in popularity as classification, prediction, and clustering tools, particularly since they can better interpret the non-linear patterns of spectral data [ 51 , 52 , 53 , 54 ]. Machine learning (ML) modeling based on ANN can be trained from a set of given data known as ‘inputs’ or independent variables and form complex, non-linear relationships with these inputs and the ‘targets’ or dependent variables [ 54 ]. For example, preliminary ML models for the classification of smoke tainted grapevines have been developed using infra-red (IR) thermal imagery from canopies, which gave an indication of changes in stomatal conductance for classification of control and smoke-exposed grapevines [ 25 ].…”