Assessing the Quality of Wheat Flour Blended with Cassava Starch Using a Handheld NIR Spectrophotometer and Chemometrics

Candeias, Dâmaris N. C.; Barros, Sara Regina R. C. de; Lyra, Wellington S.; Fernandes, David Douglas S.; Diniz, Paulo Henrique G. D.

doi:10.21577/0103-5053.20240090

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Journal of the Brazilian Chemical Society

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Article • J. Braz. Chem. Soc. 36 (1) • 2025 • https://doi.org/10.21577/0103-5053.20240090 copy

Assessing the Quality of Wheat Flour Blended with Cassava Starch Using a Handheld NIR Spectrophotometer and Chemometrics

Authorship SCIMAGO INSTITUTIONS RANKINGS

This work developed an effective and eco-friendly analytical method to support quality control for Brazilian Bill proposal No. 5332/2009. The bill advocates the creation of the “Brazilian bread” by mandating the incorporation of cassava byproducts into wheat flour, with the aim of reducing Brazil’s external dependency on wheat imports and promoting domestic cassava family farming. For this, a handheld near-infrared (NIR) spectrophotometer was utilized for on-site determination of cassava starch in commercial wheat flour. The quantitative analysis, which utilized NIR spectra pre-processed with offset correction (OFF) combined with linear baseline correction (LBC), alongside the successive projections algorithm for interval selection in partial least squares (iSPA-PLS), exhibited superior predictive capability, achieving a relative error of prediction below 8%. Furthermore, the classification process, which involved NIR spectra pre-processed with LBC and iSPA-PLS for discriminant analysis (iSPA-PLS-DA), yielded an impressive 94% overall accuracy, with only 3 misclassifications in the test set.

Keywords:
quality control; wheat flour; near-infrared spectroscopy; variable selection; successive projections algorithm

	Parameter
	RMSECV / (mg kg^–1)	r_CV	RMSEP / (mg kg^–1)	r_Pred	RPD	REP / %	LV
Offset correction (OFF)
PLS	1.341	0.989	1.614	0.983	5.440	10.110	9
SPA-MLR (7)^a	3.454	0.984	4.631	0.957	3.204	7.058	–
iSPA-PLS (15/8)^a	2.381	0.963	1.473	0.989	5.960	9.228	7
Linear baseline correction (LBC)
PLS	1.525	0.986	1.506	0.987	5.470	9.076	8
SPA-MLR (8)^a	2.614	0.957	2.807	0.940	2.934	16.921	–
iSPA-PLS (5/4)^a	1.634	0.984	1.362	0.989	6.046	8.211	8
Offset correction coupled with linear baseline correction (OFF + LBC)
PLS	1.439	0.987	1.458	0.984	5.511	8.461	10
SPA-MLR (11)^a	2.266	0.968	4.429	0.837	1.814	25.706	–
iSPA-PLS (20/12)^a	2.009	0.975	1.376	0.986	5.838	7.987	8
Multiplicative scatter correction (MSC)
PLS	2.107	0.970	1.805	0.981	5.054	11.665	7
SPA-MLR (7)^a	3.295	0.927	3.871	0.906	2.357	25.017	–
iSPA-PLS (10/8)^a	1.973	0.974	1.805	0.982	5.054	11.666	7
Standard normal variate (SNV)
PLS	2.107	0.970	1.805	0.981	5.054	11.665	7
SPA-MLR (7)^a	3.308	0.926	3.873	0.906	2.356	25.030	–
iSPA-PLS (5/4)^a	2.268	0.966	1.772	0.982	5.150	11.449	7
Savitzky-Golay derivative (SGD)
PLS	1.686	0.981	2.009	0.978	4.429	11.958	7
SPA-MLR (10)^a	2.045	0.972	2.894	0.955	3.074	17.227	–
iSPA-PLS (20/18)^a	1.641	0.982	1.889	0.981	4.711	11.242	7

Pre-processing technique	PLS-DA				SPA-LDA				iSPA-PLS-DA
Pre-processing technique	Training		Test		Training		Test		Training		Test
Offset correction (OFF)
	30	–	11	4	21	9	8	7	30	–	11	4
	–	56	2	22	4	52	2	22	–	56	1	23
Variables in the model^a	9				6				9 (5/3)^b
Sensitivity / %	100		73.3		70.0		53.3		100		73.3
Specifcity / %	100		92.9		92.9		91.7		100		95.8
Effciency / %	100		82.1		80.6		69.9		100		83.8
Accuracy / %	100		84.6		84.9		76.9		100		87.2
MCC	1.00		0.67		0.66		0.50		1.00		0.73
Linear baseline correction (LBC)
	28	2	13	2	22	8	11	4	29	1	12	3
	4	52	2	22	8	48	7	17	2	54	–	24
Variables in the model^a	6				4				5 (15/4)^b
Sensitivity / %	93.3		86.7		73.3		73.3		96.7		80.0
Specifcity / %	92.9		91.7		85.7		70.8		96.4		100.0
Effciency / %	93.1		89.1		79.3		72.1		96.5		89.4
Accuracy / %	93.0		89.7		81.4		72.0		96.5		92.3
MCC	0.85		0.78		0.66		0.50		0.92		0.84
Offset correction coupled with linear baseline correction (OFF + LBC)
	30	–	11	4	29	1	11	4	30	–	12	3
	2	54	3	21	8	48	5	19	1	55	2	22
Variables in the model^a	8				7				16 (5/ 3)^b
Sensitivity / %	100		73.3		96.7		73.3		100		80.0
Specifcity / %	96.4		87.5		85.7		79.2		98.2		91.7
Effciency / %	98.2		80.1		91.0		76.2		99.1		85.6
Accuracy / %	97.7		82.0		89.5		76.9		98.8		87.2
MCC	0.95		0.62		0.59		0.43		0.97		0.73
Multiplicative scatter correction (MSC)
	30	–	10	5	22	8	8	7	30	–	11	4
	–	56	1	23	5	51	5	19	–	56	2	22
Variables in the model^a	15				3				10 (5/3)^b
Sensitivity / %	100		66.7		73.3		53.3		100		73.3
Specifcity / %	100		95.8		91.1		79.2		100		91.7
Effciency / %	100		79.9		81.7		65.0		100		82.0
Accuracy / %	100		84.6		84.9		69.2		100		84.6
MCC	1.00		0.68		0.66		0.34		1.00		0.67
Standard normal variate (SNV)
	30	–	11	4	22	8	8	7	30	–	11	4
	–	56	3	21	5	51	5	19	–	56	3	21
Variables in the model^a	16				3				9 (20/7)^b
Sensitivity / %	100		73.3		73.3		53.3		100		73.3
Specifcity / %	100		87.5		91.1		79.2		100		87.5
Effciency / %	100		80.1		81.7		65.0		100		80.1
Accuracy / %	100		82.0		84.9		69.2		100		82.0
MCC	1.00		0.62		0.66		0.34		1.00		0.62
Savitzky-Golay derivative (SGD)
	29	1	14	1	29	1	13	2	29	1	13	2
	4	52	3	21	4	52	4	20	3	53	4	20
Variables in the model^a	6				10				6 (5/4)^b
Sensitivity / %	96.7		93.3		96.7		86.7		96.7		86.7
Specificity / %	92.9		87.5		92.9		83.3		94.6		83.3
Efficiency / %	94.7		90.4		94.7		85.0		95.6		85.0
Accuracy / %	94.2		89.7		94.2		84.6		95.3		84.6
MCC	0.88		0.79		0.88		0.69		0.90		0.69

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