A sucrose absorption band in the 900–930 nm range

"A number of researchers have shown that the SSC and:or DM of a number of fruits can be
predicted by NIR spectroscopy (e.g. Birth et al., 1985; Dull et al., 1989; Kawano et al., 1992;
Slaughter, 1995). Generally, NIR light from 800 to 1000 nm has been used and success attributed to a sucrose absorption band in the 900–930 nm range."

(V.Andrew McGlone, Sumio Kawano, Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy, Postharvest Biology and Technology, Volume 13, Issue 2, April 1998, Pages 131-141, ISSN 0925-5214)

Second-derivative absorbances at 874 and 902 nm had a high correlation with the sugar content of melons

"Abstract
A method for visualizing the sugar content in the flesh of melons was developed. This method was based on the sugar absorption band in the near-infrared (NIR) region to avoid bias caused by the color information of a sample. NIR spectroscopic analysis revealed that each of the two second-derivative absorbances at 874 and 902 nm had a high correlation with the sugar content of melons. A high-resolution cooled charged couple device camera with band-pass filters, which included the above two wavelengths, was used to capture the spectral absorption image of a half-cut melon. A color distribution map of the sugar content on the surface of the melon was constructed by applying the NIR spectroscopy theory to each pixel of the acquired images."

(J Agric Food Chem. 2002 Jan 2;50(1):48-52.

Near-infrared imaging spectroscopy based on sugar absorption band for melons.

Tsuta M, Sugiyama J, Sagara Y.)

Discussion on scatter plot having discontinuous data

"Due to the randomized data split process for calibration and prediction sets, some
graphs show discontinuity (e.g. the TA between 0.14 and 0.18 g/kg). However, the lake of
data observed in the prediction results (Fig. 5) may affect in the calibration results (Table 8)."

(Liu, Y., & Ying, Y. (2007). Noninvasive method for internal quality evaluation of pear fruit using fiber-optic FT-NIR spectrometry. International Journal of Food Properties, 10(4), 877-886.)

SDR = SD/RMSEP: SDR = 2 would mean two group sorting but with much larger error overlap

"A further statistic, SDR, was also used and defined as:
SDR = SD/RMSEP
where SD was the data set standard deviation. In theory (for large normally distributed data set) it is equal to, and indicates more directly than either R2 and RMSEP separately can, the relative predictive performance of a model: the higher the value, the greater the power. We consider a value of three the minimum for sorting/grading purposes, enabling the data set to be split into three groups (of width equivalent to RMSEP) with the extreme two groups not overlapping appreciably. A value of two would mean two groups but with much larger error overlap"

(McGlone, V.A. and Kawano, S., (1998) Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy, Postharvest Biology and Technology, 13, 131-141.)

Wavelength of 914 nm was sensitive to the SSC of satsuma mandarins

"However, it is likely that the wavelengths 650-680 nm for SSC mainly attributed to the color of bayberry juice. It is same to the sensitive wavelengths to acidity of 685-695 nm, because there is nonexistent of organic acids in this region of the spectrum. So in our research, to SSC, wavelengths 970-990 nm might be of particular importance, and to acidity, 910-925 nm were better. This found was similar to the earlier literature, such as He (1998) found a wavelength of 914 nm was sensitive to the SSC of satsuma mandarins. And near 900 nm were sensitive wavelengths corresponding to organic acid of oranges."

(Shao, Y. and He. Yong (2007) Nondestructive measurement of the internal quality of bayberry juice using Vis/NIR spectroscopy, Journal of Food Engineering, 79, 1015-1019.)

Important wavelengths can be determined from PLS regression coefficients and difference in spectra

"The wavelengths important in classifying the species of interest were determined based on PLS regression coefficients and differences in spectra."

"The regression coefficients indicating important wavelengths in the calibration model are shown in Fig. 1."

(Fengyou, J., Maghirang, E., Dowell, F. Abel, C. and Ramaswamy, S., 2007. Differentating tobacco budworm and corn earworm using near-infrared spectroscopy, Journal of Economic Entomology, Vol. 100, No. 3, 759-763.)

SDR > 2.5 allows for sorting into two grades

"The SDR_P statistic for approximately 100 samples was typically above 2.5, which allows for sorting into two grades of sweetness."

(Golic, M.; Walsh, K.B. Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content. Analytica Chimica Acta 2006, 555, 286-291.)