Qualitative and quantitative detection of honey adulterated with high-fructose corn syrup and maltose syrup by using near-infrared spectroscopy.

Near-infrared spectroscopy (NIR) was used for qualitative and quantitative detection of honey adulterated with high-fructose corn syrup (HFCS) or maltose syrup (MS). Competitive adaptive reweighted sampling (CARS) was employed to select key variables. Partial least squares linear discriminant analysis (PLS-LDA) was adopted to classify the adulterated honey samples. The CARS-PLS-LDA models showed an accuracy of 86.3% (honey vs. adulterated honey with HFCS) and 96.1% (honey vs. adulterated honey with MS), respectively. PLS regression (PLSR) was used to predict the extent of adulteration in the honeys. The results showed that NIR combined with PLSR could not be used to quantify adulteration with HFCS, but could be used to quantify adulteration with MS: coefficient (Rp2) and root mean square of prediction (RMSEP) were 0.901 and 4.041 for MS-adulterated samples from different floral origins, and 0.981 and 1.786 for MS-adulterated samples from the same floral origin (Brassica spp.), respectively.

[Qualitative and quantitative detection of beet syrup adulteration of honey by near-infrared spectroscopy: a feasibility study].

In order to further investigate the utility of near-infrared spectroscopys (NIRS) in rapidly detecting honey adulteration, near-infrared spectroscopy in combination with chemometric methods was investigated for qualitative and quantitative detection of beet syrup adulteration of honey. Total prediction accuracy of testing set was 90.2% by partial least squares-discriminant analysis (PLS-DA) for authentic and adulterated honey samples. Total prediction accuracy of testing sets was all below 33.3% by different discriminant methods for classes of adulteration level. The quantitative analysis of adulteration level by PLS regression gave satisfying results if adulterated honey samples were got from the same one authentic honey sample: correlation coefficient (r)of actual values versus predicted values was 0.9829 and root mean square error of prediction (RMSEP) was 1.394 2 in testing set, otherwise it gave dissatisfying results for the adulterated samples from different botanical origins or the different samples of the same botanical origins. The results showed that NIRS could be applied for rapid detection of authentic and adulterated honey samples, but not for detection of classes of adulteration level and quantification of adulteration level with beet syrup.

Authentication of pure camellia oil by using near infrared spectroscopy and pattern recognition techniques.

Total of 4 pattern recognition methods for the authentication of pure camellia oil applying near infrared (NIR) spectroscopy were evaluated in this study. Total of 115 samples were collected and their authenticities were confirmed by gas chromatography (GC) in according to China Natl. Standard (GB). A preliminary study of NIR spectral data was analyzed by unsupervised methods including principal component analysis (PCA) and hierarchical cluster analysis (HCA). Total of 2 supervised classification techniques based on discriminant analysis (DA) and radical basis function neural network (RBFNN) were utilized to build calibration model and predict unknown samples. In the wavenumber range of 6000 to 5750 cm⁻¹, correct classification rate of both supervised and unsupervised solutions all can reach 98.3% when smoothing, first derivative, and autoscaling were used. The good performance showed that NIR spectroscopy with multivariate calibration models could be successfully used as a rapid, simple, and nondestructive method to discriminate pure camellia oil.

[Prediction analysis of soluble solids content and moisture in honey by near infrared spectroscopy].

A new method for the analysis of soluble solids content (SSC) in honey by near infrared spectroscopy (NIR) was developed, and moisture was also analyzed. The partial least square regression models of SSC and moisture were built for different pretreatments of the raw spectra in different spectral range. Good predictions were always obtained for all models. The best models of SSC and moisture were obtained by using Norris (3,2) smoothing + first derivative + multiplicative signal correction in total spectral range. The coefficient of determination (R(CV)2) and root mean square error of cross validation (RMSECV), the coefficient of determination (R(p)2) and root mean square error of validation sets (RMSEP) were 0.9986, 0.190, 0.9985 and 0.127 respectively for SSC, while for moisture they were 0.9984, 0.187, 0.9986 and 0.125 respectively. NIR could be used to analyze SSC and moisture in honey. The result of this article was better than that of related documents for moisture.

[Extraction and determination of essential oils in Indocalamus latifolius leaves and Indocalamus tessellatus leaves].

Indocalamus leaves are the generic name of the leaves of Indocalamus (Graminales) plants. The essential oils in Indocalamus latifolius leaves and Indocalamus tessellatus leaves were extracted by steam distillation method. Ether was used as the solvent to extract volatile compounds many times. Both the volatile compounds in Indocalamus latifolius leaves and Indocalamus tessellatus leaves were analyzed by gas chromatography-mass spectrometry (GC-MS). The results showed that thirty-seven compounds were identified for the essential oils extracted from Indocalamus latifolius leaves, and its main components were found to be 3-hexen-1-ol, (Z)-; 1-hexanol; benzyl alcohol; hexanal; furan, 2-ethyl-; 3-buten-2-one, 4-(2,6, 6-trimethyl-1-cyclohexen-1-yl), (E)-; etc. Forty-nine components were identified for the essential oils extracted from Indocalamus tessellatus leaves, and its main components were found to be 3-hexen-1-ol, (Z)-; benzyl alcohol; 3-buten-2-one, 4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-, (E)-; 2-hexenal; phenylethyl alcohol, 2-methoxy-4-vinylphenol; furan, 2-ethyl-; etc. 3-Hexen-1-ol, (Z)- was the most abundant compound in the essential oils from both Indocalamus leaves. There were ketone, aldehyde, alcohol, phenol and ester in them. The contents of ketones, aldehydes, and alcohols were found higher than those of other compounds in the two essential oils.