Rapid and Green Method Forensic Authentication of Rice Using Near-Infrared Spectroscopy (NIRS)

Abstract

Rice is one of the most consumed cereals in the world. Currently, techniques for the authentication and geographical origin of rice is known not to be objective because to depend on the naked eye of a well-trained inspector. DNA fingerprint methods have been shown to be inappropriate for on-site application because the method needs a lot of labor and skilled expertise. Rice consumers want to confirm cultivation origin because they believe price or eating score has a high correlation according to them. Considering rice as a raw material of economic and social value and the recent use of NIR spectroscopy coupled with chemometric methods to authentication and discrimination of geographical origin as an alternative to classical methods in the search for a methodology in line with Green Chemistry, this work investigates the potential of NIR spectroscopy combined with multivariate analysis: PCA (Principal Component Analysis) and HCA (Hierarchical Cluster Analysis) for rapid and non-destructive forensic authentication of rice grains from Brazil and Venezuela. This study investigated the potential of near-infrared spectroscopy, combined with PCA and HCA chemometric technique to the authenticity of rice. It was verified that is feasible and advantageous to implement authenticity detection of different brands, typology and geographical discrimination (Brazil and Venezuela) rice.

Fast and Green Method to Control Frauds of Geographical Origin in Traded Cuttlefish Using a Portable Infrared Reflective Instrument

“…The classification of foodstuff combining NIRS with chemometric approaches has already been considered as valid method to discriminate the origin of vegetable products, such as asparagus [43] and rice [44], and in fishery product, as observed in sea cucumber [25], sea bass [26], tilapia [27], and anchovies [41]. In detail, in the present study, the capability of the NIRS to discriminate cuttlefish according to the FAO fishing area was assessed comparing two among the most common chemometric techniques used in food authentication and adulteration [45], i.e., SVM and KNN models.…”

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

“…The same authors proposed 12 principles that would guide Green Chemistry: one-Prevention; two-Atom Economy; three-Less Hazardous Chemical Syntheses; four-Designing Safer Chemicals; five-Safer Solvents and Auxiliaries; six-Design for Energy Efficiency; seven-Use of Renewable Feedstocks; eight-Reduction of Derivatives; nine-Catalysis; 10-Design for Degradation; eleven-Real-Time Analysis for Pollution Prevention and twelve-Inherently Safer Chemistry for Accident Prevention. These principles had the function of maximizing the efficiency of human and financial resources in order to minimize environmental and human health risks [1][2][3].…”

“…The most important principle is the first (because avoiding waste is better than treating or cleaning), the waste generated; the task of scientists, especially analytical chemists, is to develop new methods or techniques that can reduce or eliminate the consumption of reagents and minimize the work time of analysts through more efficient and effective routine analyzes because of the sustainable development of society [2] In 2015, the United Nations General Assembly created the 2030 Agenda for Sustainable Development, in which it established 17 sustainable development goals broken down into 169 goals to be achieved by the year 2030. Researchers in the field of Chemistry play a role important for Sustainable Development Goals Three, Six and Nine.…”

“…Several works have been published in the literature using near-infrared spectroscopy and the use of Machine Learning to predict chemical attributes of grains and classify rice, soy, sesame, edible oils, plants and Amazonian oils [2,[7][8][9][10][11][12][13]. Furthermore, it stands out for not requiring any preparation of the samples besides not using reagents, and it does not destroy or modify the sample.…”