Ensemble machine learning approach for electronic nose signal processing

Electronic nose (e-nose) systems have been reported to be used in many areas as rapid, low- cost, and non-invasive instruments. Especially in meat production and processing, e-nose system is a powerful tool to process volatile compounds as a unique ‘fingerprint’. The ability of the pattern recognition algorithm to analyze e-nose signals is the key to the success of the e-nose system in many applications. On the other hand, ensemble methods have been reported for favorable performances in various data sets. This research proposes an ensemble learning approach for e-nose signal processing, especially in beef quality assessment. Ensemble methods are not only used for learning algorithms but also sensor array optimization. For sensor array optimization, three filter-based feature selection algorithms (FSAs) are used to build ensemble FSA such as reliefF, chi-square, and gini index. Ensemble FSA is developed to deal with different or unstable outputs of a single FSA on homogeneous e-nose data sets in beef quality monitoring. Moreover, ensemble learning algorithms are employed to deal with multi-class classification and regression tasks. Random forest and Adaboost are used that represent bagging and boosting algorithms, respectively. The results are also compared with support vector machine and decision tree as single learners. According to the experimental results, our ensemble approach has good performance and generalization in e-nose signal processing. Optimized sensor combination based on filter-based FSA shows stable results both in classification and regression tasks. Furthermore, Adaboost as a boosting algorithm produces the best prediction even though using a smaller number of sensor

Colorimetric sensor arrays for the detection of aqueous and gaseous analytes

The past decade has seen great interest concerning the development of artificial sensing devices; most notably optoelectronic tongues and noses. Utilizing previous research on how the mammalian gustatory and olfactory systems operate, significant progress in mimicking these systems has been realized. The turning point in this field of research has been the discovery that the mammalian senses of smell and taste are not based on specific receptors for each stimulant, but rather an array of semi-specific receptors that function simultaneously to produce a pattern. This pattern is interpreted in the brain, and classified either as a known stimulant or a new analyte similar to a known family of tastes or odors. As a predominantly visual species, we are programmed to acknowledge visible reports to chemical reactions over alternative reporting methods. Thus, colorimetric sensing can be more advantageous than other techniques and can allow for a greater number of chemical reactions to be probed. One colorimetric approach to sensing involves the immobilization of cross-responsive chemosensors capable of showing a color change upon reaction with analytes or mixtures of analytes. The employment of porous glasses as an immobilization technique has allowed for facile detection of analytes, both aqueous and gaseous, by allowing dye-analyte interactions to occur while preventing the sensor dye from escaping from the matrix. In this manner, colorimetric sensor arrays have been fashioned that are capable of discriminating among structurally similar compounds such as sugars, while retaining the ability to detect a wide range of analytes including toxic industrial chemicals. For aqueous detection, the newly developed porous glasses successfully immobilized otherwise soluble dyes that could detect changes in solution pH, caused by boronic acid-diol interactions. This allowed for rapid and sensitive detection and identification of natural and artificial sugars and sweeteners. Further experiments showed the array’s ability to differentiate between a selection of common table-top sweeteners such as Equal®, Sweet’N’Low®, Splenda®, and natural sugars. Gas sensing applications were made possible by slight modifications to the liquid sensing array. Hydrophobic silica precursors were added to limit the effect of changing humidity on the array, and printing onto flat, non-porous polymer surfaces gave fast and easy accessibility of incoming analytes to the immobilized indicators. Stable and sensitive colorimetric arrays for the detection and semi-quantification of a large number of toxic industrial chemicals was made possible by the inclusion of additional indicators capable of colorimetrically reporting changes in polarity, metal ligation, and redox reactions. The performances of these sensing arrays showed extremely low limits of detection, and were capable of identifying toxic gases within a large range of concentrations; ppb up to concentration immediately dangerous to life and health. In order to improve upon the detection limits for weakly responding gaseous analytes, alternative methods were developed. It was found that the immobilization of simple and stable color-changing dyes within chemically-reactive matrices could allow for facile and sensitive detection and quantification of formaldehyde. Optimization studies were carried out to assess the proper doping level of hydrophilic polymers with amine-appended polyethylene glycol

Real-time gas mass spectroscopy by multivariate analysis

Early and significant results for a real-time, column-free miniaturized gas mass spectrometer in detecting target species with partial overlapping spectra are reported. The achievements have been made using both nanoscale holes as a nanofluidic sampling inlet system and a robust statistical technique. Even if the presented physical implementation could be used with gas chromatography columns, the aim of high miniaturization requires investigating its detection performance with no aid. As a study case, in the first experiment, dichloromethane (CH2Cl2) and cyclohexane (C6H12) with concentrations in the 6-93 ppm range in single and compound mixtures were used. The nano-orifice column-free approach acquired raw spectra in 60 s with correlation coefficients of 0.525 and 0.578 to the NIST reference database, respectively. Then, we built a calibration dataset on 320 raw spectra of 10 known different blends of these two compounds using partial least square regression (PLSR) for statistical data inference. The model showed a normalized full-scale root-mean-square deviation (NRMSD) accuracy of [Formula: see text] and [Formula: see text] for each species, respectively, even in combined mixtures. A second experiment was conducted on mixes containing two other gasses, Xylene and Limonene, acting as interferents. Further 256 spectra were acquired on 8 new mixes, from which two models were developed to predict CH2Cl2 and C6H12, obtaining NRMSD values of 6.4% and 13.9%, respectively

A review of optical nondestructive visual and near-infrared methods for food quality and safety

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.©2013 the Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Spartan Daily, November 22, 1999

Volume 113, Issue 59https://scholarworks.sjsu.edu/spartandaily/9488/thumbnail.jp

Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment

In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products

Sensory quality control of alcoholic beverages using fast chemical sensors

Control de calidad sensorial de bebidas alcohólicas utilizando rápidos sensores químicosEn la presente tesis Doctoral, han sido aplicados dos sensores artificiales para el análisis debebidas alcohólicas: la nariz electrónica basada en la espectrometría de masas (MS) y la lenguaelectrónica basada en la espectroscopía infrarroja con transformada de Fourier (FTIR). Elpropósito fue desarrollar nuevas estrategias para analizar la autenticidad de estos productos,desde un punto de vista sensorial, por medio de técnicas las espectrales antes mencionadas.Adicionalmente, ha sido utilizado un espectrofotómetro UV-visible como ojo electrónico. Eltrabajo presentado pretende ser un avance significativo hacia el desarrollo de un catadorelectrónico mediante la fusión de los tres sensores químicos: nariz electrónica, lenguaelectrónica y ojo electrónico.Sensory quality control of alcoholic beverages using fast chemical sensorsIn the present Doctoral Thesis, two chemical artificial sensors are applied to the analysis ofalcoholic beverages: the Mass Spectrometry (MS)-based electronic-noses and Fouriertransform infrared (FTIR)-based electronic-tongue. The aim was developing new strategies totest the authenticity of these products, from a sensory point of view, by means of the spectraltechniques above mentioned. Additionally, has been used an UV-visible spectrophotometer aselectronic eye. The work presented wants to be a significant advance towards the developmentof an electronic taster through the fusion of three chemical sensors: electronic nose, electronictongue and electronic eye

Tool wear monitoring in turning using fused data sets of calibrated acoustic emission and vibration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The main aim of this research is to develop an on-line tool wear condition monitoring intelligent system for single-point turning operations. This is to provide accurate and reliable information on the different states of tool wear. Calibrated acoustic emission and vibration techniques were implemented to monitor the progress of wear on carbide tool tips. Previous research has shown that acoustic emission (AE) is sensitive to tool wear. However, AE, as a monitoring technique, is still not widely adopted by industry. This is because it is as yet impossible to achieve repeatable measurements of AE. The variability is due to inconsistent coupling of the sensor with structures and the fact that the tool structure may have different geometry and material property. Calibration is therefore required so that the extent of variability becomes quantifiable, and hence accounted for or removed altogether. Proper calibration needs a well-defined and repeatable AE source. In this research, various artificial sources were reviewed in order to assess their suitability as an AE calibration source for the single-point machining process. Two artificial sources were selected for studying in detail. These are an air jet and a pulsed laser; the former produces continuous-type AE and the latter burst type AE. Since the air jet source has a power spectrum resembling closely the AE produced from single-point machining and since it is readily available in a machine shop, not to mention its relative safety compared to laser, an air-jet source is a more appealing choice. The calibration procedure involves setting up an air jet at a fixed stand-off distance from the top rake of the tool tip, applying in sequence a set of increasing pressures and measuring the corresponding AE. It was found that the root-mean-square value of the AE obtained is linearly proportional to the pressure applied. Thus, irrespective of the layout of the sensor and AE source in a tool structure, AE can be expressed in terms of the common currency of 'pressure' using the calibration curve produced for that particular layout. Tool wear stages can then be defined in terms of the 'pressure' levels. In order to improve the robustness of the monitoring system, in addition to AE, vibration information is also used. In this case, the acceleration at the tool tip in the tangential and feed directions is measured. The coherence function between these two signals is then computed. The coherence is a function of the vibration frequency and has a value ranging from 0 to 1, corresponding to no correlation and full correlation respectively between the two acceleration signals. The coherence function method is an attempt to provide a solution, which is relatively insensitive to the dynamics and the process variables except tool wear. Three features were identified to be sensitive to tool wear and they are; AErms, and the coherence function of the acceleration at natural frequency (2.5-5.5 kHz) of the tool holder and at high frequency end (18-25kHz) respectively. A belief network, based on Bayes' rule, was created providing fusion of data from AE and vibration for tool wear classification. The conditional probabilities required for the belief network to operate were established from examples. These examples were presented to the belief network as a file of cases. The file contains the three features mentioned earlier, together with cutting conditions and the tool wear states. Half of the data in this file was used for training while the other half was used for testing the network. The performance of the network gave an overall classification error rate of 1.6 % with the WD acoustic emission sensor and an error rate of 4.9 % with the R30 acoustic emission sensor.Funding was obtained from The Royal Thai Government, the Petroleum Authority of Thailand and King Mongkut's University of Technology Thonburi (KMUTT

Sorting of Coffee Beans for 'Potato Defect' in East African Countries

Since ancient times, coffee has been a savory drink for most of the world's population. It is the second most widely distributed commodity after crude oil in the world. Hence, there has always been a pressure on the coffee industry to produce more volume of good quality coffee. The coffee industry has not been able to meet this increasing coffee demand due to various reasons, such as low crop yield, high coffee rejection rate etc. Historically, the coffee production industry has had high rejection rates due to inadequate knowledge about the defects that plague coffee and the lack of research to detect and eliminate the defective coffee beans. In this thesis, an attempt has been made to minimize the rejection rate of coffee beans due to a specific defect called "Potato Defect". Potato defect is very prominent in East African countries for reasons not yet known. It is caused by an increase in the concentration of 2-isopropyl -3-methoxypyrazine (IPMP), present in parts per billion concentration in coffee beans. In this thesis, various techniques have been evaluated to detect the increased concentration of IPMP, and then eliminate the 'potato defect' infected coffee beans. As these proposed techniques need to be implemented on an industrial scale, special care has been taken to keep the inspection time of coffee beans as low as possible to minimize its negative impact on the overall coffee production rate. Considering both sensitivity and time, non destructive methods such as ion mobility spectrometry, cavity ring down spectrometry and electronic nose were assessed for their suitability to identify low concentrations of IPMP in the complex matrix of coffee volatiles. Experiments were also conducted by Solid Phase Micro Extraction (SPME), followed by multidimensional gas chromatography with simultaneous olfactory and mass spectrometric detection (GC- MS-O) technology to validate information related to the 'potato defect'. GC-MS-O could detect IPMP present in whole green coffee beans while other researchers only detected IPMP in ground coffee. The findings of this thesis opens the doors for the coffee industry to establish non destructive, sensitive methodology to analyze further coffee aroma