Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR)
Determination of soluble solid content (SSC) and pH of banana was investigated using a modular Vis/NIR spectroscopy in reflectance mode. Vis/NIR spectroscopy has been applied for non-destructive SSC or pH measurement, but limited studies were conducted for a modular VIS/NIR spectroscopy. This study...
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| Fformat: | Conference or Workshop Item |
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2021
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| author | Masithoh, R.E. Pahlawan, M.F.R. Wati, R.K. |
| author_facet | Masithoh, R.E. Pahlawan, M.F.R. Wati, R.K. |
| author_sort | Masithoh, R.E. |
| collection | UGM |
| description | Determination of soluble solid content (SSC) and pH of banana was investigated using a modular Vis/NIR spectroscopy in reflectance mode. Vis/NIR spectroscopy has been applied for non-destructive SSC or pH measurement, but limited studies were conducted for a modular VIS/NIR spectroscopy. This study was conducted to develop a calibration model to predict SSC and pH in bananas using a modular type of VIS/NIR spectroscopy at wavelength of 350-1000 nm. Two chemometrics analysis namely partial least square (PLS) and principle component regression (PCR) were used to develop calibration models and to predict SSC and pH of bananas. Normalization, baseline correction, standard normal variate (SNV), and multiplicative scatter correction (MSC) pre-processing were used for spectra transformation. Research showed that PLS regression produced better models compared to PCR in determining SSC and pH contents. PLS regression resulted in RC2 of 0.95, RMSEC of 1.27, Rp2 of 0.85, RMSEP of 1.98, and bias of -0.09 for SSC and RC2 of 0.96, RMSEC of 0.05, Rp2 of 0.82, RMSEP of 0.11, and bias of 0.11 for pH. PCR resulted in RC2 of 0.78, RMSEC of 2.63, Rp2 of 0.76, RMSEP of 2.71, and bias of -0.12 for SSC and RC2 of 0.71, RMSEC of 0.14, Rp2 of 0.62, RMSEP of 0.16, and bias of -0.02 for pH. This modular Vis/NIR instrument combined with proper pre-processing method and chemometric analysis is promising to be used for determination of SSC and pH of fruits. © Published under licence by IOP Publishing Ltd. |
| first_indexed | 2024-03-14T00:03:22Z |
| format | Conference or Workshop Item |
| id | oai:generic.eprints.org:280191 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-14T00:03:22Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2801912023-11-09T06:55:36Z https://repository.ugm.ac.id/280191/ Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR) Masithoh, R.E. Pahlawan, M.F.R. Wati, R.K. Applied Statistics Food technology Statistics Determination of soluble solid content (SSC) and pH of banana was investigated using a modular Vis/NIR spectroscopy in reflectance mode. Vis/NIR spectroscopy has been applied for non-destructive SSC or pH measurement, but limited studies were conducted for a modular VIS/NIR spectroscopy. This study was conducted to develop a calibration model to predict SSC and pH in bananas using a modular type of VIS/NIR spectroscopy at wavelength of 350-1000 nm. Two chemometrics analysis namely partial least square (PLS) and principle component regression (PCR) were used to develop calibration models and to predict SSC and pH of bananas. Normalization, baseline correction, standard normal variate (SNV), and multiplicative scatter correction (MSC) pre-processing were used for spectra transformation. Research showed that PLS regression produced better models compared to PCR in determining SSC and pH contents. PLS regression resulted in RC2 of 0.95, RMSEC of 1.27, Rp2 of 0.85, RMSEP of 1.98, and bias of -0.09 for SSC and RC2 of 0.96, RMSEC of 0.05, Rp2 of 0.82, RMSEP of 0.11, and bias of 0.11 for pH. PCR resulted in RC2 of 0.78, RMSEC of 2.63, Rp2 of 0.76, RMSEP of 2.71, and bias of -0.12 for SSC and RC2 of 0.71, RMSEC of 0.14, Rp2 of 0.62, RMSEP of 0.16, and bias of -0.02 for pH. This modular Vis/NIR instrument combined with proper pre-processing method and chemometric analysis is promising to be used for determination of SSC and pH of fruits. © Published under licence by IOP Publishing Ltd. 2021 Conference or Workshop Item PeerReviewed Masithoh, R.E. and Pahlawan, M.F.R. and Wati, R.K. (2021) Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR). In: IOP Conference Series: Earth and Environmental Science. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107192518&doi=10.1088%2f1755-1315%2f752%2f1%2f012047&partnerID=40&md5=6f072a76ba3e91488e017349362147a0 |
| spellingShingle | Applied Statistics Food technology Statistics Masithoh, R.E. Pahlawan, M.F.R. Wati, R.K. Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR) |
| title | Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR) |
| title_full | Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR) |
| title_fullStr | Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR) |
| title_full_unstemmed | Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR) |
| title_short | Non-destructive determination of SSC and pH of banana using a modular Vis/NIR spectroscopy: Comparison of Partial Least Square (PLS) and Principle Component Regression (PCR) |
| title_sort | non destructive determination of ssc and ph of banana using a modular vis nir spectroscopy comparison of partial least square pls and principle component regression pcr |
| topic | Applied Statistics Food technology Statistics |
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