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Abstract

In this study, near infrared spectroscopy has been demonstrated to quickly determine the saponin content in soapnut fruits. Partial least squares analysis combined with pre-processing methods and significance multivariate correlation variable selection was introduced to develop a statistical model calibrated for saponin content in soapnut fruits. The results showed that the first derivative yielded the best partial least squares calibration models with spectra of both the surface of dried fruits and the powder of dry seeded fruits with root mean square error of calibration values of 0.85% and 0.59%, respectively. The surface model presented less accuracy than the powder model. However, when the significance multivariate correlation variable selection method was applied to select the best variables from the spectra, the partial least squares models using spectra of surface and powder samples became similar, with higher R² values (0.84 and 0.90), lower root mean square error of calibration values of 0.23% and 0.39%. It was suggested that near infrared spectroscopy could be a promising and rapid method for predicting the saponin content in the soapnut fruits without grinding them into powder.

Keywords

Significance multivariate correlation partial least squares saponin content nondestructive analysis near infrared spectroscopy

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References

Dembitsky

. Astonishing diversity of natural surfactants: 1. Glycosides of fatty acids and alcohols. Lipids 2004; 39: 933–953.

Takagi

Park

Kato

. Anti-inflammatory activities of hederagenin and crude saponin isolated from Sapindus mukorossi Gaertn. Chem Pharm Bull 1980; 28: 1183–1188.

Petersen

Palmqvist

. Utilizing column selectivity in developing a high-performance liquid chromatographic method for ginsenoside assay. J Chromatogr A 1990; 504: 139–149.

Kasai

Yamaguchi

Tanaka

. High-performance liquid chromatography of glycosides on a new type of hydroxyapatite column. J Chromatogr A 1987; 407: 205–210.

Zhang

et al.

An improved facile method for extraction and determination of steroidal saponins in Tribulus terrestris by focused microwave-assisted extraction coupled with GC-MS. J Sep Sci 2009; 32: 4167–4175.

Huang

H-C

Liao

S-C

Chang

F-R

et al.

Molluscicidal saponins from Sapindus mukorossi, inhibitory agents of golden apple snails, Pomacea canaliculata. J Agric Food Chem 2003; 51: 4916–4919.

Libby

Stettler

Seitz

. Forest genetics and forest-tree breeding. Annu Rev Genet 1969; 3: 469–494.

Roberts

Workman

Reeves

. Near-infrared spectroscopy in agriculture, Madison: American Society of Agronomy, 2004.

Williams

Norris

. Near-infrared technology in the agricultural and food industries, St. Paul, Minnesota: American Association of Cereal Chemists, Inc., 1987. .

10.

Jamshidi

Minaei

Mohajerani

et al.

Prediction of soluble solids in oranges using visible/near-infrared spectroscopy: effect of peel. Int J Food Prop 2014; 17: 1460–1468.

11.

Bureau

Ruiz

Reich

et al.

Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chem 2009; 113: 1323–1328.

12.

ElMasry

Iqbal

Sun

D-W

et al.

Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system. J Food Eng 2011; 103: 333–344.

13.

Xiaojing

Pinyan

et al.

Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination. Anal Chim Acta 2009; 634: 166–171.

14.

Mehmood

Liland

Snipen

et al.

A review of variable selection methods in partial least squares regression. Chemometr Intell Lab Syst 2012; 118: 62–69.

15.

Tran

Afanador

Buydens

et al.

Interpretation of variable importance in partial least squares with significance multivariate correlation (sMC). Chemometr Intell Lab Syst 2014; 138: 153–160.

16.

Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013.

17.

Savitzky

Golay

. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 1964; 36: 1627–1639.

18.

Lee

Chang

Jun

C-H

et al.

Kernel-based calibration methods combined with multivariate feature selection to improve accuracy of near-infrared spectroscopic analysis. Chemometrics Intellig Lab Syst 2015; 147: 139–146.

19.

Heng

Ling

et al.

Extraction and fermentation-based purification of saponins from Sapindus mukorossi Gaertn. J Surfact Deterg 2015; 18: 429–438.

20.

Baranska

Schütze

Schulz

. Determination of lycopene and β-carotene content in tomato fruits and related products: comparison of FT-Raman, ATR-IR, and NIR spectroscopy. Anal Chem 2006; 78: 8456–8461.

21.

Baranski

Baranska

Schulz

. Changes in carotenoid content and distribution in living plant tissue can be observed and mapped in situ using NIR-FT-Raman spectroscopy. Planta 2005; 222: 448–457.

22.

Kawano

Watanabe

Iwamoto

. Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode. J Jpn Soc Hortic Sci 1992; 61: 445–451.

23.

Liu

Y-d

Ying

Y-b

et al.

Experiments on predicting sugar content in apples by FT-NIR Technique. J Food Eng 2007; 80: 986–989.

24.

Osborne

Douglas

. Measurement of the degree of starch damage in flour by near infrared reflectance analysis. J Sci Food Agric 1981; 32: 328–332.

25.

Liu

Sun

Ouyang

. Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci Technol 2010; 43: 602–607.

Prediction of saponin content in soapnut ( Sapindus mukorossi Gaertn.) fruits by near infrared spectroscopy

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