Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm -1 ), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm -1 to 4570.9 cm -1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.
Short-wavelength near infrared spectroscopy (SW-NIR) is a very rapid, versatile and precise technique, which can be used in many different situations and for very types of products and chemical compounds. Extended multiplicative signal correction (EMSC) is a modification of the standard MSC pre-processing method that allows the separation of physical light scattering effects from chemical (vibrational) light absorbance effects in spectra. In this paper, the EMSC is applied and compared with first derivate, second derivate, MSC and SNV in combination of PLSR to obtain robust models in terms of accuracy and predict ability with a reduced calibration data set using SW-NIR spectra of moisture in marzipan. The Extended Multiplicative Signal Correction-EMSC and combination methods provide the best results in terms of prediction ability and calibration SW-NIR spectra of moisture in marzipan. The best classification results were obtained by Extended Multiplicative Signal Correction followed by second derivates.
Foram analisadas amostras de quiabo dos municípios de Caruaru e Vitória de Santo Antão, em Pernambuco, assim como nos municípios de Ceará-Mirim, Macaíba e Extremoz no estado do Rio Grande do Norte. A aplicação de dois métodos de análise exploratória de dados: Análise de Componentes principais - PCA e Análise de Agrupamentos Hierárquicos - HCA permitiu a discriminação geográfica do quiabo proveniente dos dois estados.
The differentiation of cultivars is carried out by means of morphological descriptors, in addition to molecular markers. In this work, near-infrared spectroscopy (NIR) and chemometric techniques were used to develop classification models for two different commercial sesame cultivars (Sesamum indicum) and 3 different strains. The diffuse reflectance spectra were recorded in the region of 700 to 2500 nm. Based on the application of chemometric techniques: principal component analysis—PCA, hierarchical cluster analysis—HCA, k-nearest neighbor—KNN and the flexible independent modeling of class analogy—SIMCA, from the infrared spectra in the near region, it was possible to perform the genotyping of two sesame cultivars (BRS Seda and BRS Anahí), and to classify these cultivars with 3 different sesame strains, obtaining 100% accurate results. Due to the good results obtained with the implemented models, the potential of the methods for a possible realization of forensic, fast and non-destructive authentication, in intact sesame seeds was evident.
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.
This work presents an analytical technique, using chemometrics in data obtained by Nearinfrared, which has the potential to assist Forensic Science in the geographical
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