scholarly journals Use of Near-Infrared Spectroscopy and Chemometrics for the Nondestructive Identification of Concealed Damage in Raw Almonds (Prunus dulcis)

2016 ◽  
Vol 64 (29) ◽  
pp. 5958-5962 ◽  
Author(s):  
Cristian Rogel-Castillo ◽  
Roger Boulton ◽  
Arunwong Opastpongkarn ◽  
Guangwei Huang ◽  
Alyson E. Mitchell
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prunus Dulcis ◽  
Nondestructive Identification

scholarly journals Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sergio Borraz-Martínez ◽  
Joan Simó ◽  
Anna Gras ◽  
Mariàngela Mestre ◽  
Ricard Boqué
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prunus Dulcis ◽  
Selectivity Ratio ◽  
Plant Industry ◽  
Classification Rate ◽  
Data Set ◽  
Quality Control Tool ◽  
Nursery Plant

AbstractThe emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used. The most important variables to discriminate between these varieties were studied through of three scientifically accepted indicators (Variable importance in projection¸ selectivity ratio and vector of the regression coefficients). The results showed that the 7000 to 4000 cm−1 range contains the most useful variables, which allowed to decrease the complexity of the data set. Concerning to the classification models, a high percentage of correct classifications (90–100%) was obtained, where dried-powdered leaves showed better results than fresh leaves. However, the classification rate of both kinds of leaves evidences the capacity of the near-infrared spectroscopy to discriminate Prunus dulcis varieties. We demonstrate with these results the capability of the NIRS technology as a quality control tool in nursery plant industry.

scholarly journals Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy

Talanta ◽  
2019 ◽  
Vol 204 ◽  
pp. 320-328 ◽  
Author(s):  
Sergio Borraz-Martínez ◽  
Ricard Boqué ◽  
Joan Simó ◽  
Mariàngela Mestre ◽  
Anna Gras
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prunus Dulcis

Fast Nondestructive Identification of Endothelium Corneum Gigeriae Galli Using Visible/Near-Infrared Spectroscopy

2011 ◽  
Vol 28 (12) ◽  
pp. 120701 ◽  
Author(s):  
Xiao-Yan Zhang ◽  
Yao-Yong Meng ◽  
Hao Zhang ◽  
Wen-Juan Ou ◽  
Song-Hao Liu
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nondestructive Identification

Food authentication: Determination of the geographical origin of almonds (Prunus dulcis Mill.) via near-infrared spectroscopy

2021 ◽  
Vol 160 ◽  
pp. 105702 ◽  
Author(s):  
Maike Arndt ◽  
Marc Rurik ◽  
Alissa Drees ◽  
Christian Ahlers ◽  
Simon Feldmann ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Geographical Origin ◽  
Prunus Dulcis ◽  
Food Authentication

Rapid and Nondestructive Identification of Different Pure Yarn Fabrics Based on near Infrared Spectroscopy

2015 ◽  
Vol 671 ◽  
pp. 363-368 ◽  
Author(s):  
Cai Hong Wang ◽  
Hua Yong Liu ◽  
Xiong Ying Wu ◽  
Xue Mei Ding
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rejection Rate ◽  
Combustion Method ◽  
Rapid Testing ◽  
Testing Methods ◽  
Mean Centering ◽  
Data Points ◽  
Nondestructive Identification

Near infrared spectroscopy combined with chemometrics analysis were investigated as an emerging method for the identification of textiles, this method allowed straightforward and rapid testing of textiles without destroying their integrity compared to traditional testing methods such as dissolution method, combustion method and microscope observation. In this work, different pretreating algorithms coupled with the Soft Independent Modelling by Class Analogy (SIMCA) have been studied to achieve the best recognition model to identify different pure yarn fabrics (cotton, linen, silk, wool and polyester). Results showed that little difference between different data points smoothing. Percentages of recognition and rejection of 100% were obtained of silk, wool and polyester by pretreating with Savitzky-Golay 7 data points smoothing, Savitzky-Golay second derivative and mean centering. The percentages of recognition were 93.75% and 96.25% for cotton and linen respectively by pretreating with Savitzky-Golay 7 data points smoothing, Savitzky-Golay first derivative and mean centering, nevertheless, the percentages of rejection was low for linen with 83.75%. The results from this paper suggested that near infrared spectroscopy in combination with SIMCA could be applied to the identification of pure yarn fabrics, whereas further study should be made to improve the rejection rate of linen.

Sign in / Sign up

Export Citation Format

Share Document