Determination of colorant type in yellow tofu using Vis-NIR and SW-NIR spectroscopy

RAHMAWATI, Laila; WIDODO, Slamet; KURNIADI, Deni Permana; DAUD, Pamungkas; TRIYONO, Agus; SRIHARTI,; SUSANTI, Novita Dwi; MAYASTI, Nur Kartika Indah; INDRIATI, Ashri; YULIANTI, Lista Eka; PUTRI, Devry Pramesti; KUALA, Seri Intan; ANGGARA, Cahya Edi Wahyu; PRISTIANTO, Eko Joni; KURNIAWAN, Erry Dwi; APRIYANTO, Ignatius Fajar; KURNIAWAN, Dayat

doi:10.1590/fst.112422

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Original Article • Food Sci. Technol 43 • 2023 • https://doi.org/10.1590/fst.112422 copy

Determination of colorant type in yellow tofu using Vis-NIR and SW-NIR spectroscopy

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

A method that can detect colorant type in food is requisite against illegal practices that used non-food-grade colorants in food. In this study colorant types of yellow tofu were identified using visible near-infrared (Vis-NIR) and shortwave near-infrared spectra (SW-NIR) spectroscopy. Chemometrics analysis using partial least square-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) were used to classify natural colorants, non-food-grade colorants, and food-grade colorants in yellow tofu. The best model obtained using Vis-NIR had calibration accuracy and reliability of 100% for both PCA-LDA and PLS-DA. The best model yielded by SW-NIR had calibration accuracy of 76% and 28% reliability for PLS-DA, and accuracy of 92% and 76% reliability for PCA-LDA. The result showed that Vis-NIR spectroscopy was superior to SW-NIR spectroscopy to distinguish the colorant type used in yellow tofu. Moreover, PCA-LDA yielded better accuracy and reliability compared to PLS-DA.

Keywords:
food colorant; adulteration; dyes; tofu; chemometrics analysis

Colorant type	R (Red)	G (Green)	B (Blue)
Natural colorant (NC)	207 ± 13.3	165 ± 21.1	3 ± 0.5
Food-grade colorant (FGC)	214 ± 3.5	126 ± 0.6	5 ± 2.6
Non-Food-grade colorant (NFGC)	202 ± 4	172 ± 4.2	3 ± 1

True Class	Predicted Class	Calibration							Prediction
		n	Predicted sample						n		Predicted sample
		n	Raw	Norm	SGD1	SGD2	SNV	MSC				Raw	Norm	SGD1	SGD2	SNV	MSC
NC	NC	91	91	91	91	91	91	91		90		89	90	90	90	90	90
NC	FGC	0	0	0	0	0	0	0		0		0	0	0	0	0	0
NC	NFGC	0	0	0	0	0	0	0		0		1	0	0	0	0	0
FGC	NC	0	0	0	0	0	0	0		0		0	0	0	0	0	0
FGC	FGC	90	90	90	90	90	90	90		90		90	90	90	90	90	90
FGC	NFGC	0	0	0	0	0	0	0		0		0	0	0	0	0	0
NFGC	NC	0	0	0	0	0	0	0		0		0	0	0	0	0	0
NFGC	FGC	0	0	0	0	0	0	0		0		0	0	0	0	0	0
NFGC	NFGC	89	89	89	89	89	89	89		90		90	90	90	90	90	90
Acc (%)			100	100	100	100	100	100				100	100	100	100	100	100
Reliability (%)			100	100	100	100	100	100				99	100	100	100	100	100

True Class	Predicted Class	Calibration								Prediction
		n	Predicted sample							n		Predicted sample
		n	Raw	Norm	SGD1	SGD2	SNV	MSC			Raw		Norm	SGD1	SGD2	SNV	MSC
NC	NC	90	76	81	58	71	67	66	90		67		74	53	71	67	67
NC	FGC	0	7	5	3	16	1	1	0		11		9	5	16	6	6
NC	NFGC	0	7	4	29	3	22	23	0		12		7	32	3	17	17
FGC	NC	0	4	8	4	3	10	10	0		3		7	2	3	7	7
FGC	FGC	90	77	78	78	82	77	77	90		75		79	78	80	75	75
FGC	NFGC	0	9	4	8	5	3	3	0		12		4	10	7	8	8
NFGC	NC	0	0	0	2	0	2	2	0		0		1	1	0	4	4
NFGC	FGC	0	1	1	3	1	3	3	0		0		0	0	0	2	2
NFGC	NFGC	90	89	89	85	89	85	85	90		90		89	89	90	84	84
Acc (%)			90	92	82	90	85	84			86		90	81	89	84	84
Reliability (%)			69	76	46	69	54	53			58		69	44	68	51	51

True Class	Predicted Class	Calibration								Prediction
		n	Predicted sample							n		Predicted sample
		n	Raw	Norm	SGD1	SGD2	SNV	MSC			Raw		Norm	SGD1	SGD2	SNV	MSC
NC	NC	91	91	90	91	91	91	90	90		89		89	89	90	90	89
NC	FGC	0	0	0	0	0	0	0	0		1		0	1	0	0	0
NC	NFGC	0	0	0	0	0	0	0	0		0		0	0	0	0	0
FGC	NC	0	0	0	0	0	0	0	0		0		0	0	0	0	0
FGC	FGC	90	90	90	90	90	90	90	90		90		90	90	90	90	90
FGC	NFGC	0	0	0	0	0	0	0	0		0		0	0	0	0	0
NFGC	NC	0	0	0	0	0	0	0	0		0		0	0	0	0	0
NFGC	FGC	0	0	0	0	1	0	0	0		0		0	0	1	0	0
NFGC	NFGC	89	89	89	89	88	88	88	90		89		89	90	89	90	89
Not classified		0	0	1	0	0	1	2	0		1		2	0	0	0	2
Acc (%)			100	100	100	100	100	99			99		99	100	100	100	99
Reliability (%)			100	99	100	99	99	98			98		98	99	99	100	98

True Class	Predicted Class	Calibration								Prediction
		n	Predicted sample							n		Predicted sample
		n	Raw	Norm	SGD1	SGD2	SNV	MSC			Raw		Norm	SGD1	SGD2	SNV	MSC
NC	NC	91	51	55	57	61	60	60	90		51		53	57	53	63	63
NC	FGC	0	40	35	34	30	29	29	0		38		35	31	34	24	24
NC	NFGC	0	0	0	0	0	0	0	0		0		0	1	1	1	1
FGC	NC	0	11	9	9	7	3	4	0		15		6	12	7	8	8
FGC	FGC	90	79	78	78	83	86	85	90		74		82	77	82	81	80
FGC	NFGC	0	0	3	3	0	1	1	0		1		2	1	1	1	2
NFGC	NC	0	0	1	1	0	1	1	0		1		0	0	0	0	1
NFGC	FGC	0	34	29	32	23	26	31	0		33		32	32	24	28	28
NFGC	NFGC	89	52	57	55	61	59	55	90		52		55	56	62	59	58
Not classified		0	3	3	1	5	5	4	0		5		5	3	6	5	5
Acc (%)			67	70	70	76	76	74			66		70	70	73	75	74
Reliability (%)			2	11	11	28	28	22			-3		11	11	19	26	23

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[1] *Corresponding author: laila.rahmawati53@gmail.com