Noninvasive Method for Internal Quality Evaluation of Pear Fruit Using Fiber-Optic FT-NIR Spectrometry

Figures & data

Figure 1 Schematic diagram of the setup for FT-NIR measurement of pear fruit in the wavelength of 800–2500 nm.

Figure 2 Typical spectra of log (1/R) and the first derivate of the reflectance reciprocal, D₁ log (1/R) for pear fruit.

Table 1 Summary statistics of parameters of pear samples in this experiment

Parameter	n ^a	Mean	SD	CV (%)	Range
SSC (°brix)	248	12.817	0.698	5.450	10.980–14.30
TA (g/kg)	248	0.139	0.033	23.606	0.253–0.095
^an, number of Pear samples selected for each attribute.

Table 2 Means, ranges, and SDs of pear samples of calibration and validation sets

Parameter	No. factors ^a	n ^b	Mean	SD	CV (%)	Range
SSC (°brix) Cal	10	166	12.840	0.729	5.675	14.340–10.980
Val		82	12.77	0.634	4.97	14.300–11.150
TA (g/kg) Cal	7	166	0.140	0.034	24.351	0.253–0.095
Val		82	0.139	0.0317	22.124	0.234–0.098
^aNo. factors, number of factors determined by cross-validation of calibration samples.
^bn, number of pear samples selected for each parameter.

Table 3 Correlation coefficients between SSC and TA parameters about 248 pear samples

Parameter	SSC	TA
SSC (°brix)	1.000	−0.604
TA (g/kg)	−0.604	1.000

Table 4 The results for SSC and TA calibration models of PLS regression methods for log (1/R) using different spectral correction in the wavelength range of 814–1834 nm

Parameter	Spectral correction	No. of factors	Correlation	RMSEC	RMSEP
SSC (%)	MSC	10	0.936	0.252	0.329
	SNV	8	0.900	0.312	0.336
	Undecided	10	0.915	0.287	0.325
TA (%)	MSC	7	0.815	0.0195	0.0186
	SNV	7	0.816	0.0195	0.0187
	Undecided	8	0.817	0.0194	0.0185

Table 5 The results for calibration models of PLS regression methods for the log (1/R), means values of RMSEC, RMSEP, and RMSECV, the correlation coefficients of determination, r with 814–1834 nm, 1155–1834 nm, and 814–1155 nm wavelength ranges

Parameter	Wavelength range (nm)	No. of factors	Correlation	RMSECV	RMSEC	RMSEP
SSC	814–1834	10	0.936	0.362	0.256	0.320
	1155–1834	10	0.908	0.377	0.303	0.368
	814–1155	9	0.951	0.477	0.225	0.395
TA	814–1834	7	0.815	0.022	0.020	0.019
	1155–1834	7	0.808	0.022	0.020	0.020
	814–1155	6	0.837	0.023	0.018	0.020

Table 6 The results for calibration models of PLS and PCR methods for the log (1/R), first derivative D ₁log (1/R), and its second derivative D ₂ log (1/R), means values of RMSEC and the coefficients of determination, r ²

Parameter	Regression method	Statistical parameter	Spectrum
			Log (1/R)	D1 log (1/R)	D2 log (1/R)
SSC	PLS	No. of factors	10	7	7
		r^2	0.876	0.905	0.904
		RMSEC	0.256	0.224	0.226
	PCR	No. of factors	8	7	3
		r^2	0.706	0.723	0.420
		RMSEC	0.394	0.382	0.555
TA	PLS	No. of factors	7	4	3
		r^2	0.665	0.696	0.676
		RMSEC	0.020	0.019	0.019
	PCR	No. of factors	2	5	4
		r^2	0.488	0.588	0.584
		RMSEC	0.024	0.022	0.022

Figure 3 Calibrations of partial least square regression by the FT-NIR system versus laboratory measurements of soluble solids content of pear fruit.

Table 7 The results for prediction errors of PLS regression method and r ², the coefficients of determination for the log (1/R) based on about 33% of the data set, for SSC and TA parameters

	SSC		TA
Parameter	RMSEP	r^2	RMSEP	r^2
log (1/R)	0.320	0.751	0.0186	0.6291
D1 log (1/R)	0.332	0.739	0.0196	0.6114
D2 log (1/R)	0.370	0.684	0.0198	0.5993

Figure 4 Predictions of partial least square regression by the FT-NIR system versus laboratory measurements of soluble solids content of pear fruit.

Figure 5 Predictions of partial least square regression by the FT-NIR system versus laboratory measurements of titratable acidity of pear fruit.

Table 8 Slope, offset, correlation, root mean standard error of cross validation (RMSECV) and number of PLS factors for best regression models for the log (1/R), based on about 33% of the data set, for SSC and TA parameters

Parameter	Slope	Offset	Correlation	RMSECV	No. factors
SSC	0.905	−1.318	0.866	0.361	10
TA	0.906	−0.014	0.793	0.021	7