Cozzolino D, Kwiatkowski M J, Waters E J, Gishen M
The Australian Wine Research Institute, P.O. Box 197, Glen Osmond, SA 5064, Australia.
Anal Bioanal Chem. 2007 Mar;387(6):2289-95. doi: 10.1007/s00216-006-1031-z. Epub 2007 Jan 4.
The aim of this study was to explore the capability of spectroscopy in the visible (Vis) and short wavelength near-infrared (NIR) regions for the non-destructive measurement of wine composition in intact bottles. In this study we analysed a wide range of commercial wines obtained in Australia in different types of bottles (e.g. colours, diameters and heights), including different wine styles and varieties. Wine bottles were scanned in the Vis-NIR region (600-1,100 nm) in a monochromator instrument in transflectance mode. Principal component analysis (PCA) and partial least-squares (PLS) regression were used to interpret the spectra and develop calibrations for wine composition. Due to the relatively small number of samples available full cross-validation (leave-one-out) was used as validation. The coefficient of correlation in calibration [Formula: see text] and the standard error of cross-validation (SECV) were 0.67 (SECV: 0.48%), 0.83 (SECV: 4.01 mg L-1), 0.70 (SECV: 28.6 mg L-1) and 0.50 (SECV: 0.15) for alcohol content, total SO2, free SO2 and pH, respectively, in the set of wine samples analysed. These preliminary results showed that the assessment of wine composition by Vis and short wavelengths in the NIR is possible for either qualitative analysis (e.g. low-, medium- and high-quality grading), or for screening of composition during bottling and storage. Although low accuracy and precision were obtained for the chemical parameters routinely analysed in wine, calibration models for the chemical parameters were considered acceptable for screening purposes in terms of the standard errors obtained.
本研究的目的是探索可见光（Vis）和短波长近红外（NIR）区域的光谱技术对完整瓶中葡萄酒成分进行无损测量的能力。在本研究中，我们分析了在澳大利亚获得的、装在不同类型瓶子（如颜色、直径和高度）中的多种商业葡萄酒，包括不同的葡萄酒风格和品种。葡萄酒瓶在可见-近红外区域（600 - 1100 nm）使用单色仪仪器以漫反射模式进行扫描。主成分分析（PCA）和偏最小二乘法（PLS）回归用于解释光谱并建立葡萄酒成分的校准模型。由于可用样本数量相对较少，采用全交叉验证（留一法）作为验证方法。在所分析的葡萄酒样本集中，酒精含量、总二氧化硫、游离二氧化硫和pH值的校准相关系数[公式：见原文]和交叉验证标准误差（SECV）分别为0.67（SECV：0.48%）、0.83（SECV：4.01 mg/L）、0.70（SECV：28.6 mg/L）和0.50（SECV：0.15）。这些初步结果表明，利用可见光和近红外短波长对葡萄酒成分进行评估，无论是用于定性分析（如低、中、高质量分级），还是用于瓶装和储存过程中的成分筛选都是可行的。尽管对葡萄酒中常规分析的化学参数获得的准确度和精密度较低，但就所获得的标准误差而言，化学参数的校准模型用于筛选目的是可以接受的。
