Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.

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Robust PLS models for soluble solids content and firmness determination in low chilling peach using near-infrared spectroscopy (NIR)

Postharvest Biology and Technology ◽

10.1016/j.postharvbio.2015.08.006 ◽

2016 ◽

Vol 111 ◽

pp. 345-351 ◽

Cited By ~ 34

Author(s):

Paloma Andrade Martins Nascimento ◽

Lívia Cirino de Carvalho ◽

Luis Carlos Cunha Júnior ◽

Fabíola Manhas Verbi Pereira ◽

Gustavo Henrique de Almeida Teixeira

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Soluble Solids ◽

Soluble Solids Content ◽

Solids Content

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Prediction of water content and soluble solids content of ‘manalagi’ apples using near infrared spectroscopy

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/922/1/012062 ◽

2021 ◽

Vol 922 (1) ◽

pp. 012062

Author(s):

K Kusumiyati ◽

Y Hadiwijaya ◽

D Suhandy ◽

A A Munawar

Keyword(s):

Infrared Spectroscopy ◽

Water Content ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Quality Attributes ◽

Soluble Solids ◽

Coefficient Of Determination ◽

Soluble Solids Content ◽

Orthogonal Signal Correction ◽

Solids Content

Abstract The purpose of the research was to predict quality attributes of ‘manalagi’ apples using near infrared spectroscopy (NIRS). The desired quality attributes were water content and soluble solids content. Spectra data collection was performed at wavelength of 702 to 1065 nm using a Nirvana AG410 spectrometer. The original spectra were enhanced using orthogonal signal correction (OSC). The regression approaches used in the study were partial least squares regression (PLSR) and principal component regression (PCR). The results showed that water content prediction acquired coefficient of determination in calibration set (R2cal) of 0.81, coefficient of determination in prediction set (R2pred) of 0.61, root mean squares error of calibration set (RMSEC) of 0.009, root mean squares of prediction set (RMSEP) of 0.020, and ratio performance to deviation (RPD) of 1.62, while soluble solids content prediction displayed R2cal, R2pred, RMSEC, RMSEP, and RPD of 0.79, 0.85, 0.474, 0.420, and 2.69, respectively. These findings indicated that near infrared spectroscopy could be used as an alternative technique to predict water content and soluble solids content of ‘manalagi’ apples.

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