酿酒科技
釀酒科技
양주과기
LIQUOR-MAKING SCIENCE & TECHNOLOGY
2015年
4期
99-102
,共4页
蒋巧勇%吕进%刘辉军%张文君
蔣巧勇%呂進%劉輝軍%張文君
장교용%려진%류휘군%장문군
黄酒%酸度%糖度%二维相关红外光谱%模型
黃酒%痠度%糖度%二維相關紅外光譜%模型
황주%산도%당도%이유상관홍외광보%모형
yellow rice wine%acidity%sugar%two-dimensional relevant infrared spectrum%model
以市场上黄酒中的糖、酸含量及其相互比例配制糖-酸混合溶液作为研究对象，分析酸对糖的影响。从而将得到的结论用于建立更好的糖度模型。首先将酸含量作为干扰因子，利用二维红外光谱法研究酸对糖的NIR光谱的影响。结果发现，随着酸度的升高，在1800～1850 nm范围内各官能团吸收峰发生先后变化；酸的O-H、C-O分别与糖的O-H、C-O形成氢键，从而对糖的NIR光谱产生影响。其次，将所有样品（99个）作为样品集，分批次将相同酸度下的样品（11个）作为预测集建立PLS糖度模型，以相对分析误差（RPD）、相对标准偏差（RSD）作为糖度模型预测效果的优劣判断指标，研究酸度对糖度模型的影响。研究发现，随着酸度从3.5%增加到7%，糖度预测模型判断指标RPD、RSD分别从165、0.0017变成61、0.0047，模型效果变差。因此，在建立黄酒糖度模型时应考虑酸对糖的影响，从而提高黄酒糖度模型的预测能力。
以市場上黃酒中的糖、痠含量及其相互比例配製糖-痠混閤溶液作為研究對象，分析痠對糖的影響。從而將得到的結論用于建立更好的糖度模型。首先將痠含量作為榦擾因子，利用二維紅外光譜法研究痠對糖的NIR光譜的影響。結果髮現，隨著痠度的升高，在1800～1850 nm範圍內各官能糰吸收峰髮生先後變化；痠的O-H、C-O分彆與糖的O-H、C-O形成氫鍵，從而對糖的NIR光譜產生影響。其次，將所有樣品（99箇）作為樣品集，分批次將相同痠度下的樣品（11箇）作為預測集建立PLS糖度模型，以相對分析誤差（RPD）、相對標準偏差（RSD）作為糖度模型預測效果的優劣判斷指標，研究痠度對糖度模型的影響。研究髮現，隨著痠度從3.5%增加到7%，糖度預測模型判斷指標RPD、RSD分彆從165、0.0017變成61、0.0047，模型效果變差。因此，在建立黃酒糖度模型時應攷慮痠對糖的影響，從而提高黃酒糖度模型的預測能力。
이시장상황주중적당、산함량급기상호비례배제당-산혼합용액작위연구대상，분석산대당적영향。종이장득도적결론용우건립경호적당도모형。수선장산함량작위간우인자，이용이유홍외광보법연구산대당적NIR광보적영향。결과발현，수착산도적승고，재1800～1850 nm범위내각관능단흡수봉발생선후변화；산적O-H、C-O분별여당적O-H、C-O형성경건，종이대당적NIR광보산생영향。기차，장소유양품（99개）작위양품집，분비차장상동산도하적양품（11개）작위예측집건립PLS당도모형，이상대분석오차（RPD）、상대표준편차（RSD）작위당도모형예측효과적우렬판단지표，연구산도대당도모형적영향。연구발현，수착산도종3.5%증가도7%，당도예측모형판단지표RPD、RSD분별종165、0.0017변성61、0.0047，모형효과변차。인차，재건립황주당도모형시응고필산대당적영향，종이제고황주당도모형적예측능력。
In this study, sugar-acid mixed solution was prepared with the reference of acids content and sugar content and their relative ratio in market yellow rice. Firstly, acids content was used as the interference factor to investigate the effects of acids on sugar NIR spectra by two-di-mensional infrared spectroscopy. The results suggested that, with the increase of acids content, the infrared absorption peaks at 1800~1850 nm range presented certain change successively, O-H and C-O groups of acids reacted with O-H and C-O groups of sugar and further formed into hydrogen bond which influenced sugar NIR spectra. Secondly, all the samples (99) were used as sample collection, the samples (11) of the same acid content were divided into different batches as the prediction collection to construct PLS sugar content model, PRD and RSD were used as the indexes to judge the prediction effects of sugar content model, and the effects of acids on sugar content model were inves-tigated. The results suggested that, with the increase of acid content from 3.5%to 7%, the prediction effects of the model became worse (RPD changed from 165 to 61, and RSD changed from 0.0017 to 0.0047). Accordingly, the effects of acids on sugar should be considered during the construction of yellow rice wine sugar content model to improve its prediction capability.
