E-Nose Application to Food Industry Production

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Food companies worldwide must constantly engage in product development to stay competitive, cover existing markets, explore new markets, and meet key consumer requirements. This ongoing development places high demands on achieving quality at all levels, particularly in terms of food safety, integrity, quality, nutrition, and other health effects. Food product research is required to convert the initial product idea into a formulation for upscaling production with ensured significant results. Sensory evaluation is an effective component of the whole process. It is especially important in the last step in the development of new products to ensure product acceptance. In that stage, measurements of product aroma play an important role in ensuring that consumer expectations are satisfied. To this end, the electronic nose (e-nose) can be a useful tool to achieve this purpose. The e-nose is a combination of various sensors used to detect gases by generating signals for an analysis system. Our research group has investigated the scent factor in some foodstuff and attempted to develop e-noses based on low-cost technology and compact size. In this paper, we present a summary of our research to date on applications of the e-nose in the food industry.Chilo, J.; Pelegrí Sebastiá, J.; Cupane, M.; Sogorb Devesa, TC. (2016). E-Nose Application to Food Industry Production. IEEE Instrumentation and Measurement Magazine. 19(1):27-33. doi:10.1109/MIM.2016.7384957S273319

Essential oil content of cardamom (Elettaria cardamomum Maton) by Hand-held Electronic Nose

Cardamom (Elettaria cardamomum Maton.) is one of the most expensive spices in the world. Essential oil is the most functionally important component of cardamom and is described as sweet, spicy, warm, camphoraceous and citrusy. The quality of cardamom is assessed by the essential oil content and its composition. Design of hand-held E-nose and evaluation of quality of cardamom  in terms of its essential oil content is reported here

Post-consumer textile waste classification through near-infrared spectroscopy, using an advanced deep learning approach

The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating negative environmental impacts. A new textile economy based on the principles of circular economy is needed for a more sustainable textile industry. To help meet this challenge, an efficient collection, classification, and recycling system needs to be implemented at the end-of-life stage of textile products, so as to obtain high-quality recycled materials able to be reused in high-value products. This paper contributes to the classification of post-consumer textile waste by proposing an automatic classification method able to be trained to separate higher-quality textile fiber flows. Our proposal is the use of near-infrared (NIR) spectroscopy combined with a mathematical treatment of the spectra by convolutional neural networks (CNNs) to classify and separate 100% pure samples and binary mixtures of the most common textile fibers. CNN is applied for the first time to the classification of textile samples. A total of 370 textile samples were studied— 50% used for calibration and 50% for prediction purposes. The results obtained are very promising (100% correct classification for pure fibers and 90–100% for binary mixtures), showing that the proposed methodology is very powerful, able to be trained for the specific separation of flows, and compatible with the automation of the system at an industrial scale.This research was partially funded by the Ministerio de Industria, Comercio, y Turismo de España under grant number AEI-010400-2020-206, and by the Generalitat de Catalunya, under grant numbers 2017 SGR 967 and 2017 SGR 828.Peer ReviewedPostprint (published version

Identificación de muestras de papel mediante espectrometria IR y métodos multivariables

Actualmente en todo el mundo se consume una gran cantidad de papel reciclado. Ello ha provocado la fabricación de papel que contiene muchos tipos de impurezas, por lo que las empresas papeleras se están viendo con la necesidad de desarrollar métodos para controlar la calidad del papel entrante para así garantizar su calidad. Para ello en este trabajo se investiga un método muy rápido y no destructivo para identificar diferentes tipos de papel que permita desechar el papel entrante que no cumpla con unos requisitos mínimos de calidad. De este modo se puede mejorar el comportamiento de la máquina de papel y al mismo tiempo asegurar la calidad del producto final. Ello se hace en base al estudio de los espectros FTIR (espectroscopia del infrarrojo medio por transformada de Fourier) y NIR (espectroscopia del infrarrojo cercano) tratados mediante ICA (análisis de componentes independientes) como técnica multivariable de extracción de características para reducir la cantidad de variables utilizadas y k-NN (k vecinos más cercanos) como técnica de clasificación. Los resultados experimentales muestran que es posible identificar con éxito más de un 90% de las muestras estudiadas, de una forma rápida, muy automatizada y de forma no destructiva.Postprint (published version

Paper samples identification by means of IR spectrometry and multivariate

Actualmente en todo el mundo se consume una gran cantidad de papel reciclado. Ello ha provocado la fabricación de papel que contiene muchos tipos de impurezas, por lo que las empresas papeleras se están viendo con la necesidad de desarrollar métodos para controlar la calidad del papel entrante para así garantizar su calidad. Para ello en este trabajo se investiga un método muy rápido y no destructivo para identificar diferentes tipos de papel que permita desechar el papel entrante que no cumpla con unos requisitos mínimos de calidad. De este modo se puede mejorar el comportamiento de la máquina de papel y al mismo tiempo asegurar la calidad del producto final. Ello se hace en base al estudio de los espectros FTIR (espectroscopia del infrarrojo medio por transformada de Fourier)y NIR (espectroscopia del infrarrojo cercano) tratados mediante ICA (análisis de componentes independientes) como técnica multivariable de extracción de características para reducir la cantidad de variables utilizadas y k-NN k vecinos más cercanos) como técnica de clasificación. Los resultados experimentales muestran que es posible identificar con éxito más de un 90% de las muestras estudiadas, de una forma rápida, muy automatizada y de forma no destructivaPostprint (published version