The purpose of this paper is to develop FT‐NIR technique for determination of moisture content in bael pulp.
Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 70 to 95 per cent (wb). The prediction models based on partial least squares (PLS) regression, were developed in the near‐infrared region (4,000‐2,500cm‐1). Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre‐processing (vector normalization, minimum‐maximum normalization and multiplicative scatter correction) methods.
The best calibration model was developed with min‐max normalization (MMN) spectral pre‐processing (R2=99.3). The MMN pre‐processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.993 was obtained for the calibration model developed. The developed results indicated that FTNIR spectroscopy could be used for rapid detection of moisture content in bael pulp samples without any sample destruction.
The research in this paper is useful for the quick detection of moisture content of bael fruit pulp during processing.
Bag, S., Srivastav, P. and Mishra, H. (2011), "FT‐NIR spectroscopy: a rapid method for estimation of moisture content in bael pulp", British Food Journal, Vol. 113 No. 4, pp. 494-504. https://doi.org/10.1108/00070701111123970Download as .RIS
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