Near infrared spectra of honeys are affected by sample temperature variation, mainly due to a change in hydrogen bonding of water. The aim of this study was to develop robust and powerful calibration models which can compensate for a variation of sample temperature for the determination of moisture and reducing sugar content in honey using near infrared spectroscopy. Partial least squares regression with the aid of standard normal variate transformation was used to develop three calibration models at constant temperature (25, 35 and 45℃) and a robust calibration model with temperature compensation. All the developed models for moisture and reducing sugar content showed high performance of prediction with coefficient of determination (r2) and residual prediction deviation values greater than 0.95 and 3.8, respectively. The results show that the temperature compensation model can be considered as a robust calibration model for near infrared determination of moisture and reducing sugar in the honey when sample temperature is varied.
da SilvaPMGaucheCGonzagaLV. Honey: chemical composition, stability and authenticity. Food Chem2016; 196: 309.
2.
YücelYSultanogluP. Characterization of honeys from Hatay region by their physicochemical properties combined with chemometrics. Food Biosci2013; 1: 16.
3.
EscuredoOMíguezMFernández-GonzálezM. Nutritional value and antioxidant activity of honeys produced in a European Atlantic area. Food Chem2013; 138: 851.
4.
ChirifeJZamoraMCMottoA. The correlation between water activity and % moisture in honey: fundamental aspects and application to Argentine honeys. J Food Eng2006; 72: 287.
5.
DowneyGFouratierVKellyJD. Detection of honey adulteration by addition of fructose and glucose using near infrared transflectance spectroscopy. J Near Infrared Spectrosc2003; 11: 447.
6.
RuoffKLuginbühlWBogdanovS. Quantitative determination of physical and chemical measurands in honey by near-infrared spectrometry. Eur Food Res Technol2007; 225: 415.
7.
WoodcockTDowneyGKellyJD. Geographical classification of honey samples by near infrared spectroscopy: a feasibility study. J Agr Food Chem2007; 55: 9128.
8.
ChenLWangJYeZ. Classification of Chinese honeys according to their floralorigin by near infrared spectroscopy. Food Chem2012; 135: 338.
9.
ZhuXLiSShanY. Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics. J Food Eng2010; 101: 92.
10.
CozzolinoDMorónA. Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions. Soil Till Res2006; 85: 78.
11.
ŜaŝićSSegtnamVHOzakiY. Self-modeling curve resolution study of temperature-dependent near-infrared spectra of water and the investigation of water structure. J Phys Chem2002; 105: 760.
12.
SegtnanVHŠašićŠIsakssonT. Studies on the structure of water using two-dimensional near-infrared correlation spectroscopy and principal component analysis. Anal Chem2001; 73: 3153.
13.
KawanoSAbeH. Development of a calibration equation with temperature compensation for determining the Brix value in intact peaches. J Near Infrared Spectrosc1995; 3: 211.
14.
PeirsAScheerlinckNMocolaïBM. Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents. Postharvest Biol Tecnol2003; 30: 233.
15.
CozzolinoDLiuLCynkarWU. Effect of temperature variation on the visible and near infrared spectra of wine and the consequences on the partial least square calibrations developed to measure chemical composition. Anal Chim Acta2007; 588: 224.
16.
LiangXYLiXYLeiTW. Study of sample temperature compensation in the measurement of soil moisture content. Measurement2011; 44: 2200.
17.
WatariMOzakiY. Prediction of ethylene content in melt-state random and block polypropylene by near-infrared spectroscopy and chemometrics: influence of a change in sample temperature and its compensation method. Appl Spectrosc2005; 59: 600.
18.
WeiZWangJWangY. Classification of monofloral honeys from different floral origins and geographical origins based on rheometer. J Food Eng2010; 96: 469.
19.
Horwitz W and Association of Official Analytical Chemists. Official methods of analysis of AOAC International. 18th ed., Gaithersburg, Maryland: AOAC International, 2006.
20.
MurrayIAucottLSPikeIH. Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fish meal with meat and bone meal. J Near Infrared Spectrosc2001; 9: 297.
21.
GenkawaTShinzawaHKatoH. Baseline correction of diffuse reflection near-infrared spectra using searching region standard normal variate (SRSNV). Appl Spectrosc2015; 69: 1432.
22.
OsborneBFearnTHindlePH. Practical NIR spectroscopy with applications in food and beverage analysis, Harlow, UK: Longman Scientific and Technical, 1993.
23.
Williams P. Near-infrared technology – getting the best out of light. A short course in the practical implementation of near-infrared spectroscopy for the user. Nanaimo, Canada: PDK Projects, Inc., 2008.
24.
Codex Alimentarius. Codex standard 12-1981, revised Codex Standard for Honey. Standards and standard methods. Rome: FAO, 2001.
25.
MoreiraRFAMariaCABPietroluongoM. Chemical changes in the volatile fractions of Brazilian honeys during storage under tropical conditions. Food Chem2010; 121: 697.
26.
Gallardo-VelázquezTOsorio-RevillaGLoaMZ-d. Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys. J Food Res2009; 42: 313.
27.
DowneyGFouratierVKellyJD. Detection of honey adulteration by addition of fructose and glucose using near infrared transfectance spectroscopy. J Near Infrared Spectrosc2003; 11: 447.
28.
LatorreCHCrecenteRMPMartínSG. A fast chemometric procedure based on NIR data for authentication of honey with protected geographical indication. Food Chem2013; 141: 3559.
29.
PeinadoACvan den BergFBlancoM. Temperature-induced variation for NIR tensor-based calibration. Chemometr Intell Lab2006; 83: 75.
30.
JensenPSBakJAnderson-EngelsS. Influence of temperature on water and aqueous glucose absorption spectra in the near- and mid-infrared regions at physiologically relevant temperatures. Appl Spectrosc2003; 57: 28.
31.
QiuPYDingHBTangYK. Determination of chemical composition of commercial honey by near-infrared spectroscopy. J Agric Food Chem1999; 47: 2760.
32.
García-AlvarezMHuidobroJFHermidaM. Determination of chemical composition of commerciac honey by near-infrared spectroscopy. J Agric Food Chem2000; 48: 5154.
33.
AcharyaUKWalshKBSubediPP. Robustness of partial least-squares models to change in sample temperature: I. A comparison of methods for sucrose in aqueous solution. J Near Infrared Spectrosc2014; 22: 279.
34.
AcharyaUKWalshKBSubediPP. Robustness of partial least-squares models to change in sample temperature: II. Application to fruit attributes. J Near Infrared Spectrosc2014; 22: 287.
