Direct orthogonal signal correction as data pretreatment in the classification of clinical lots of creams from near infrared spectroscopy data

2007 ◽  
Vol 582 (1) ◽  
pp. 181-189 ◽  
Author(s):  
J. Luypaert ◽  
S. Heuerding ◽  
D.L. Massart ◽  
Y. Vander Heyden
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Spectroscopy Data ◽  
Orthogonal Signal ◽  
Direct Orthogonal Signal Correction ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Data Pretreatment

Application of OSC in Sugar Content Evaluation of Chestnut Based on near Infrared Spectroscopy

2014 ◽  
Vol 701-702 ◽  
pp. 577-580
Author(s):  
Jie Liu ◽  
Xiao Yu Li ◽  
Wei Wang ◽  
Jun Zhang ◽  
Wei Zhou
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Sugar Content ◽  
Model Performance ◽  
Nir Spectroscopy ◽  
Orthogonal Signal ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Validation Set

It is important in chestnut industry to evaluate the sugar content of nuts since sugar content is one of parameters for classifying the fruit to different productions. Previous work had proved the near infrared (NIR) spectroscopy could be used to measuring the sugar content in intact and peeled chestnut nondestructively; however, the performance of the predictive model would need more improvement. In this work, the orthogonal signal correction (OSC) algorithm was employed to optimize the predictive models. The results shown that, for the peeled chestnut sample, OSC could increase the correlation coefficient (R2) of validation set from 0.8649 to 0.8961while decrease the root mean and square error of prediction from 0.739 to 0.626. For the intact chestnut sample, this algorithm did not improve the model performance. The results indicated that the OSC had potential to optimized the prediction accuracy of sugar content in chestnut based on near infrared spectroscopy.

scholarly journals A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1586
Author(s):  
Pao Li ◽  
Xinxin Zhang ◽  
Shangke Li ◽  
Guorong Du ◽  
Liwen Jiang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Nondestructive Method ◽  
Linear Discriminant ◽  
Outer Skin ◽  
Data Pretreatment ◽  
Pca Method

Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.

scholarly journals Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden

Agroteknika ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 67-74
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Cucumis Melo L

Kadar air merupakan salah satu atribut kualitas yang penting pada komoditas hortikultura. Penetapan kadar air buah melon dengan metode konvensional memakan waktu yang lama dan perlu merusak sampel buah. Penelitian ini bertujuan untuk memprediksi kadar air buah melon golden menggunakan teknologi visible-near infrared spectroscopy (Vis-NIRS). Metode koreksi spektra orthogonal signal correction (OSC) diterapkan pada spektra original untuk meningkatkan kehandalan model kalibrasi. Partial least squares regression (PLSR) digunakan sebagai metode pendekatan regresi untuk mengekstraksi data spektra Vis-NIRS. Hasil penelitian membuktikan bahwa Vis-NIRS dapat diandalkan untuk memprediksi kadar air buah melon golden. Metode koreksi spektra OSC mampu memperkecil jumlah principal component (PC) pada spektra original. Linieritas pada model kalibrasi menggunakan spektra OSC tercatat memperoleh nilai tertinggi sebesar 0,92. Ratio of performance to deviation (RPD) pada spektra OSC menampilkan nilai tertinggi pula yaitu 3,63. Model kalibrasi yang diperoleh pada penelitian ini dapat ditransfer ke dalam spektrometer Vis-NIRS untuk prediksi kadar air melon golden secara cepat dan simultan.

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