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Principal Component Regression in Noninvasive Pineapple Soluble Solids Content Assessment Based On Shortwave Near Infrared Spectrum
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chia, Kim Seng Rahim, H. Abdul Rahim, Ruzairi Abdul |
| Copyright Year | 2013 |
| Abstract | The Principal component regression (PCR) is a combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and implemented in the non-destructive assessment of the soluble solid content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation (RMSECV) of 0.8323 Brix° with principal components (PCs) of 14. Keywords—Pineapple, Shortwave near infrared, Principal component regression, Non-invasive measurement; Soluble solids content |
| Starting Page | 533 |
| Ending Page | 536 |
| Page Count | 4 |
| File Format | PDF HTM / HTML |
| Volume Number | 7 |
| Alternate Webpage(s) | http://waset.org/publications/13682/principal-component-regression-in-noninvasive-pineapple-soluble-solids-content-assessment-based-on-shortwave-near-infrared-spectrum |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
