为探讨不同类型稻米外观品质、加工品质与酿酒专用水稻淀粉特性的关系,筛选快速鉴定指标,建立专用品种评价体系,本研究以17个籼型、10个粳型、11个籼粳型共38个水稻品种为试验材料,对稻米直链淀粉、支链淀粉和总淀粉含量、加工品质、外观品质共12个指标进行检测分析,并以直链淀粉含量 ≥ 24%为阈值,通过相关性分析、主成分分析等统计学方法进行综合评价。结果表明,籼型稻米的直链淀粉含量与支链淀粉、总淀粉含量、垩白粒率、垩白度间存在显著正相关,与整精米率间存在显著负相关;通过主成分分析,进一步筛选得到支链淀粉、总淀粉含量、垩白粒率、垩白度、整精米率、透明度共6个酒用籼型稻米鉴定指标,并确定各指标阈值为:直链淀粉含量 ≥ 24.00%,总淀粉含量 ≥ 81.46%,支链淀粉含量 ≥ 58.39%,整精米率 ≤ 81.83% ,垩白粒率 ≥ 72.8%,垩白度 ≥ 18.91%,透明度 ≥ 47.98%。约束粳型和籼粳型稻米支链淀粉含量的指标与籼型稻米存在明显差异,并依此尝试对上述各鉴定指标阈值进行预测。结果可为不同类型的酿酒专用水稻品种快速筛选和适宜原材料采购评估提供基础,为专用型水稻品种选育提供理论依据。 In order to explore the relationship between the appearance quality and processing quality of different types of rice and the starch characteristics of special rice for brewing, screen rapid identification indexes and establish a special variety evaluation system, 17 indica rice varieties, 10 japonica rice varieties, and 11 indica/japonica types were used as experimental materials. A total of 12 indexes of amylose, amylopectin and total starch content, processing quality and appearance quality of rice were detected and analyzed. Taking amylose content ≥ 24 % as the threshold, the correlation analysis and principal component analysis were used for comprehensive evaluation. The results showed that there was a significant positive correlation between amylose content and amylopectin, total starch content, chalky grain rate and chalkiness degree, and a significant negative correlation with head rice rate; through principal component analysis, six identification indexes of wine indica rice were further screened, including amylopectin, total starch content, chalky grain rate, chalkiness degree, head rice rate and transparency. The thresholds of each index were determined as follows: amylopectin content ≥ 24.00%, total starch content ≥ 81.46%, amylose content ≥ 58.39%, head rice rate ≤ 81.83%, chalky grain rate ≥ 72.8%, Chalkiness ≥ 18.91%, transparency ≥ 47.98%. The indexes restricting amylopectin content of japonica and indica/japonica rice were significantly different from that of indica rice, and the thresholds of the above identification indexes were also predicted. The results can provide a basis for rapid screening of different types of rice varieties for brewing and evaluation of suitable raw material procurement, and provide a theoretical basis for the breeding of special rice varieties.
为探讨不同类型稻米外观品质、加工品质与酿酒专用水稻淀粉特性的关系,筛选快速鉴定指标,建立专用品种评价体系,本研究以17个籼型、10个粳型、11个籼粳型共38个水稻品种为试验材料,对稻米直链淀粉、支链淀粉和总淀粉含量、加工品质、外观品质共12个指标进行检测分析,并以直链淀粉含量 ≥ 24%为阈值,通过相关性分析、主成分分析等统计学方法进行综合评价。结果表明,籼型稻米的直链淀粉含量与支链淀粉、总淀粉含量、垩白粒率、垩白度间存在显著正相关,与整精米率间存在显著负相关;通过主成分分析,进一步筛选得到支链淀粉、总淀粉含量、垩白粒率、垩白度、整精米率、透明度共6个酒用籼型稻米鉴定指标,并确定各指标阈值为:直链淀粉含量 ≥ 24.00%,总淀粉含量 ≥ 81.46%,支链淀粉含量 ≥ 58.39%,整精米率 ≤ 81.83%,垩白粒率 ≥ 72.8%,垩白度 ≥ 18.91%,透明度 ≥ 47.98%。约束粳型和籼粳型稻米支链淀粉含量的指标与籼型稻米存在明显差异,并依此尝试对上述各鉴定指标阈值进行预测。结果可为不同类型的酿酒专用水稻品种快速筛选和适宜原材料采购评估提供基础,为专用型水稻品种选育提供理论依据。
酿酒专用水稻,筛选,直链淀粉,主成分分析,水稻类型
Runzhu Luo1,2, Peiyang Jin2, Ting Huang2, Minying Chen2, Guolin Zhang2, Rui Li2, Haifang Dai1,2*, Hui Wu1,2*
1Solid-State Fermentation Resource Utilization Key Laboratory of Sichuan Province, Yibin University, Yibin Sichuan
2Faculty of Agriculture, Forestry and Food Engineering, Yibin University, Yibin Sichuan
Received: Apr. 7th, 2022; accepted: May 19th, 2022; published: May 27th, 2022
In order to explore the relationship between the appearance quality and processing quality of different types of rice and the starch characteristics of special rice for brewing, screen rapid identification indexes and establish a special variety evaluation system, 17 indica rice varieties, 10 japonica rice varieties, and 11 indica/japonica types were used as experimental materials. A total of 12 indexes of amylose, amylopectin and total starch content, processing quality and appearance quality of rice were detected and analyzed. Taking amylose content ≥ 24 % as the threshold, the correlation analysis and principal component analysis were used for comprehensive evaluation. The results showed that there was a significant positive correlation between amylose content and amylopectin, total starch content, chalky grain rate and chalkiness degree, and a significant negative correlation with head rice rate; through principal component analysis, six identification indexes of wine indica rice were further screened, including amylopectin, total starch content, chalky grain rate, chalkiness degree, head rice rate and transparency. The thresholds of each index were determined as follows: amylopectin content ≥ 24.00%, total starch content ≥ 81.46%, amylose content ≥ 58.39%, head rice rate ≤ 81.83%, chalky grain rate ≥ 72.8%, Chalkiness ≥ 18.91%, transparency ≥ 47.98%. The indexes restricting amylopectin content of japonica and indica/japonica rice were significantly different from that of indica rice, and the thresholds of the above identification indexes were also predicted. The results can provide a basis for rapid screening of different types of rice varieties for brewing and evaluation of suitable raw material procurement, and provide a theoretical basis for the breeding of special rice varieties.
Keywords:Rice Special for Brewing, Screening, Amylose, Principal Component Analysis, Rice Type
Copyright © 2022 by author(s) and Hans Publishers Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
http://creativecommons.org/licenses/by/4.0/
在传统多粮浓香型白酒酿造原料中,稻米占有较高比重 [
目前,稻米品质评价及指标筛选更多集中于稻米外观品质 [
稻米品质包括碾米品质、外观品质、蒸煮及食用品质、储藏品质等五个方面。决定稻米品质优劣主要是品种、生态环境、栽培管理以及收获干燥、贮藏加工等因素 [
本试验共选用我国各个稻区近10年来的主推水稻品种共38个,其中籼稻17个,粳稻10个,籼粳杂交稻11个,均由四川省农科院作物所提供,品种信息见表1。各品种随机选取稻种1 kg,装网袋阴干3个月后,风选去除空秕粒,去除外壳和种皮,用于检测相关参数。
类型 | 编号 | 品种名称 | 母本 × 父本 | 类型 | 编号 | 品种名称 | 母本 × 父本 |
---|---|---|---|---|---|---|---|
籼型 | V1 | 川优6203 | 川106A × 成恢3203 | 粳型 | V20 | 沈稻18 | G785 × 沈农9624 |
籼型 | V2 | 领优华占 | 领A × 华占 | 粳型 | V21 | 沈稻47 | 辽粳454 × 沈农8801 |
籼型 | V3 | 正优538 | 正902A × 天恢538 | 粳型 | V22 | 沈农9903 | 沈农89-366/辽粳454 × 沈农9741 |
籼型 | V4 | 川作优619 | 川作6A × 成恢19 | 粳型 | V23 | 皖稻68 | 武育粳2号 × 太湖糯 |
籼型 | V5 | 德优4727 | 德香074A × 成恢727 | 粳型 | V24 | 盐粳31 | 未知 |
籼型 | V6 | 内6优107 | 内香6A × 泸恢107 | 粳型 | V25 | 盐粳144 | 秋田小町/盐粳48 × 桥科951 |
籼型 | V7 | 兆优5431 | 兆A × R5431 | 粳型 | V26 | 盐粳1814 | 未知 |
籼型 | V8 | 中嘉早17 | 中选181 × 嘉育253 | 粳型 | V27 | 盐粳1813 | 未知 |
籼型 | V9 | 德香4103 | 德香074A × 泸恢H103 | 籼粳型 | V28 | 甬优6711 | 甬粳67A × F5711 |
籼型 | V10 | 神9优28 | 神9A × Q恢28 | 籼粳型 | V29 | 甬优7753 | 甬粳77A × F6853 |
籼型 | V11 | 雅7优2117 | 雅7A × 雅恢2117 | 籼粳型 | V30 | 甬优4901 | 甬粳A49 × F8001 |
籼型 | V12 | 泸两优晶灵 | 泸56S × 晶灵R | 籼粳型 | V31 | 甬优4911 | 甬粳49A/F5711 |
籼型 | V13 | 荃优1606 | 荃9311A × YR1606 | 籼粳型 | V32 | 甬优4949 | 甬粳49A × F9249 |
籼型 | V14 | 川康优6308 | 川606A × 恢1883 | 籼粳型 | V33 | 甬优4953 | 甬粳49A × F6853 |
籼型 | V15 | 美香占2号 | Lemont/丰澳占//丰澳占 | 籼粳型 | V34 | 甬优538 | 甬粳3号A × F7538 |
籼型 | V16 | 川种优607 | 川种3A × 中种R1607 | 籼粳型 | V35 | 甬优1540 | 甬粳15A × F7540 |
籼型 | V17 | 泰优390 | 泰丰A × 广恢390 | 籼粳型 | V36 | 甬优5550 | 甬粳55A × F9250 |
粳型 | V18 | 德粳4号 | 苏粳17 × 沈农9816 | 籼粳型 | V37 | 甬优2640 | 甬粳26A × F7540 |
粳型 | V19 | 沈稻2号 | 辽947 × 珍优2号 | 籼粳型 | V38 | 春优84 | 春江16A × C84 |
表1. 参试品种列表
各品种随机抽取4份样品,通过稻米品质分析系统(TPMZ-A),获取整精米率(%)、粒长(cm)、粒宽(cm)、长宽比、垩白粒率(%)、平均垩白大小、垩白度(%)、透明度(%)等参数。共4次重复。
各品种选取稻米5 g左右,通过高通量球磨仪(上海净信)研磨成粉,在40℃烘箱中烘干至恒重后,使用 SZF-06C 脂肪测定仪进行石油醚脱脂,并计算脱脂前后稻米粉干重之差,计算脂肪含量(%)。
淀粉含量的测定使用双波长法,并参照蒋卉 [
数据统计分析采用Microsoft Excel 2016进行数据整理及制表,并采用Origin2021软件进行主成分分析和线性拟合,对指标阈值进行预测。
不同水稻品种直链、直链、总淀粉含量、脂肪含量及稻米外观、加工品质指标表现见表2。直链淀粉含量由高到低排序为:籼稻 > 粳稻 > 籼粳杂交稻,其中,V3、V5、V6、V7、V8、V9、V12共7个品种的直链淀粉含量 > 24%,其余两类水稻参试品种均未达到此标准。支链淀粉和总淀粉含量排序为籼粳杂交稻 > 籼稻 > 粳稻。粒型对比发现,粳稻和籼粳杂交稻的粒长、长宽比均小于籼稻,更偏向于椭圆形,籼稻粒长大、粒宽小,呈现长粒形态。垩白表现对比发现,籼稻垩白粒率、垩白度均大幅高于其它类型水稻。总体上,各指标在不同品种间差异较大,变化趋势繁复,难以直观筛选酿酒专用水稻的评价指标。
编号 | P1 | P2 | P3 | P4 | P5 | P6 | P7 | P8 | P9 | P10 | P11 | P12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
V1 | 16.0 | 59.2 | 75.2 | 2.7 | 82.6 | 6.4 | 1.7 | 3.8 | 86.5 | 0.4 | 4.2 | 49.8 |
V2 | 14.4 | 70.0 | 84.4 | 1.2 | 85.7 | 5.8 | 2.2 | 2.7 | 99.5 | 4.3 | 50.4 | 49.4 |
V3 | 24.7 | 63.4 | 88.1 | 2.0 | 93.4 | 5.8 | 1.8 | 3.2 | 93.3 | 0.8 | 8.5 | 49.8 |
V4 | 18.5 | 42.0 | 60.5 | 1.1 | 92.8 | 6.6 | 1.8 | 3.6 | 55.6 | 0.4 | 2.6 | 49.3 |
V5 | 26.0 | 65.3 | 91.3 | 1.8 | 52.6 | 5.0 | 2.4 | 2.1 | 90.9 | 3.6 | 42.6 | 47.0 |
V6 | 24.4 | 65.8 | 90.3 | 1.6 | 70.7 | 5.1 | 2.3 | 2.2 | 100.0 | 4.0 | 47.1 | 51.0 |
V7 | 25.0 | 65.4 | 90.4 | 1.8 | 86.1 | 6.2 | 2.1 | 3.0 | 98.1 | 1.1 | 12.4 | 52.4 |
V8 | 24.5 | 63.8 | 88.2 | 2.1 | 88.4 | 5.6 | 2.8 | 2.0 | 63.2 | 1.7 | 12.8 | 46.5 |
V9 | 27.5 | 53.4 | 81.0 | 1.9 | 71.9 | 4.8 | 2.6 | 1.9 | 99.6 | 2.2 | 25.6 | 48.4 |
V10 | 15.0 | 66.1 | 81.0 | 2.6 | 92.9 | 6.0 | 2.1 | 2.9 | 29.1 | 0.2 | 0.5 | 52.3 |
V11 | 18.4 | 53.0 | 71.4 | 2.6 | 86.2 | 6.2 | 1.7 | 3.7 | 78.6 | 0.5 | 4.4 | 49.9 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
V12 | 24.6 | 68.8 | 93.3 | 2.1 | 88.6 | 6.4 | 1.8 | 3.5 | 98.3 | 1.7 | 19.6 | 50.2 |
V13 | 17.2 | 55.5 | 72.7 | 2.2 | 96.0 | 6.4 | 2.3 | 2.8 | 97.5 | 0.8 | 8.8 | 50.6 |
V14 | 15.9 | 42.9 | 58.8 | 2.3 | 85.8 | 6.2 | 2.0 | 3.2 | 93.8 | 1.0 | 10.6 | 48.2 |
V15 | 17.1 | 42.5 | 59.5 | 8.1 | 93.1 | 6.3 | 1.9 | 3.3 | 0.9 | 1.0 | 0.3 | 15.0 |
V16 | 15.7 | 50.2 | 65.9 | 1.8 | 73.8 | 6.5 | 1.8 | 3.7 | 93.3 | 1.2 | 13.5 | 49.6 |
V17 | 17.2 | 49.5 | 66.7 | 1.7 | 76.5 | 5.6 | 1.8 | 3.1 | 69.9 | 0.4 | 3.0 | 51.9 |
籼稻 | 20.1 | 57.5 | 77.6 | 2.3 | 83.4 | 5.9 | 2.1 | 3.0 | 79.3 | 1.5 | 15.7 | 47.7 |
V18 | 19.8 | 43.7 | 63.5 | 1.2 | 92.9 | 4.8 | 2.8 | 1.7 | 4.8 | 1.2 | 0.6 | 47.2 |
V19 | 21.6 | 34.3 | 55.9 | 2.0 | 91.5 | 5.3 | 2.8 | 1.9 | 16.2 | 2.0 | 3.8 | 44.7 |
V20 | 18.8 | 47.1 | 65.8 | 1.8 | 95.9 | 5.1 | 2.7 | 1.9 | 25.7 | 1.7 | 5.1 | 44.7 |
V21 | 19.4 | 52.1 | 71.5 | 1.4 | 96.3 | 5.3 | 2.7 | 1.9 | 14.0 | 1.4 | 2.5 | 46.8 |
V22 | 18.4 | 51.6 | 70.0 | 1.8 | 96.9 | 4.8 | 2.8 | 1.7 | 10.3 | 1.7 | 1.7 | 45.2 |
V23 | 18.2 | 48.0 | 66.2 | 1.8 | 65.6 | 3.4 | 2.5 | 1.3 | 100.0 | 6.1 | 72.2 | 44.8 |
V24 | 17.8 | 48.8 | 66.6 | 1.9 | 94.1 | 4.8 | 2.8 | 1.8 | 6.6 | 0.5 | 0.4 | 48.2 |
V25 | 16.9 | 48.9 | 65.9 | 2.6 | 94.6 | 4.8 | 2.7 | 1.7 | 10.9 | 2.4 | 2.9 | 43.8 |
V26 | 17.9 | 49.2 | 67.1 | 2.0 | 93.3 | 5.3 | 2.7 | 2.0 | 26.6 | 2.0 | 6.4 | 44.7 |
V27 | 17.5 | 42.8 | 60.3 | 2.1 | 95.3 | 5.2 | 2.8 | 1.9 | 13.1 | 1.8 | 2.8 | 45.9 |
粳稻 | 18.6 | 46.7 | 65.3 | 1.8 | 91.6 | 4.9 | 2.7 | 1.8 | 22.8 | 2.1 | 9.8 | 45.6 |
V28 | 14.3 | 69.9 | 84.2 | 2.3 | 76.4 | 5.0 | 2.6 | 2.0 | 24.4 | 1.9 | 5.2 | 43.6 |
V29 | 15.1 | 67.0 | 82.1 | 1.9 | 95.9 | 5.2 | 2.4 | 2.2 | 11.1 | 3.5 | 5.1 | 39.5 |
V30 | 15.4 | 65.6 | 81.0 | 1.8 | 94.5 | 5.1 | 2.4 | 2.1 | 20.8 | 2.9 | 7.4 | 40.3 |
V31 | 11.7 | 68.8 | 80.6 | 2.1 | 96.4 | 4.9 | 2.3 | 2.1 | 12.1 | 1.5 | 2.2 | 45.0 |
V32 | 13.3 | 63.3 | 76.6 | 2.2 | 85.7 | 4.8 | 2.3 | 2.0 | 67.9 | 1.9 | 15.1 | 45.3 |
V33 | 14.8 | 62.4 | 77.2 | 2.4 | 95.8 | 5.1 | 2.4 | 2.2 | 14.3 | 3.0 | 5.2 | 40.3 |
V34 | 19.2 | 53.5 | 72.8 | 2.1 | 96.1 | 4.5 | 2.6 | 1.7 | 56.0 | 2.6 | 17.5 | 43.1 |
V35 | 16.7 | 61.8 | 78.5 | 1.3 | 82.8 | 5.0 | 2.3 | 2.1 | 26.3 | 2.4 | 7.3 | 45.7 |
V36 | 13.6 | 72.2 | 85.7 | 2.0 | 59.5 | 4.2 | 2.6 | 1.7 | 46.6 | 1.7 | 9.1 | 45.7 |
V37 | 15.5 | 66.0 | 81.5 | 1.8 | 55.6 | 4.1 | 2.4 | 1.7 | 95.7 | 3.5 | 41.8 | 44.4 |
V38 | 17.4 | 57.7 | 75.1 | 1.9 | 96.1 | 4.7 | 2.7 | 1.8 | 82.2 | 2.2 | 20.9 | 43.8 |
籼粳杂交稻 | 15.2 | 64.4 | 79.6 | 2.0 | 85.0 | 4.8 | 2.5 | 2.0 | 41.6 | 2.4 | 12.4 | 43.3 |
表2. 不同类型水稻品种淀粉含量及测试指标表现
注:P1:直链淀粉含量(%);P2:支链淀粉含量(%);P3:总淀粉含量(%);P4:脂肪含量(%);P5:整精米率(%);P6:粒长(cm);P7:粒宽(cm):P8:长宽比;P9:垩白粒率(%);P10:平均垩白大小;P11:垩白度(%);P12:透明度(%)。下同。
由图1可知,籼稻品种直链淀粉含量与支链淀粉、总淀粉含量、垩白粒率、垩白度间存在显著正相关,与整精米率间存在显著负相关,意味着上述指标与直链淀粉含量的高低具有显著、直接的关系,有可能成为酿酒专用水稻品种的快速筛选评价指标。与之相比,粳稻直链淀粉含量与支链淀粉、总淀粉、脂肪含量呈显著负相关性,籼粳杂交稻直链淀粉含量与支链淀粉、总淀粉、脂肪含量、长宽比呈显著负相关性,与粒宽、垩白粒率、垩白大小、垩白度存在显著正相关性。
图1. 不同类型水稻品种参试指标相关系数矩阵
在酿酒原料水稻中以籼稻为主要来源的现状下,根据籼稻品质特性,对粳稻、籼粳杂交稻相关指标进行预测和约束,提高其酿酒适用性是有必要的。
在上述基础上开展主成分分析,将籼稻的12个指标进一步约束为2个主成分。根据分析籼稻各指标主成分特征向量由表3可知,直链淀粉、总淀粉、支链淀粉、垩白粒率、垩白度、整精米率、透明度在主成分1中最为突出。因此,在已确定酿酒专用水稻直链淀粉不低于24%的情况下,上述6个指标可以作为酿酒专用水稻品种的快速筛选评价指标。
指标 | 主成分1 | 主成分2 | 指标 | 主成分1 | 主成分2 | 指标 | 主成分1 | 主成分2 |
---|---|---|---|---|---|---|---|---|
直链淀粉 | 0.271 | −0.039 | 垩白粒率 | 0.370 | 0.270 | 粒长 | −0.124 | 0.486 |
总淀粉 | 0.383 | 0.150 | 垩白度 | 0.410 | −0.119 | 粒宽 | −0.037 | −0.489 |
支链淀粉 | 0.355 | 0.190 | 透明度 | 0.222 | 0.086 | 长宽比 | −0.050 | 0.521 |
整精米率 | −0.360 | −0.001 | 平均垩白大小 | 0.319 | −0.296 | 脂肪含量 | −0.223 | 0.100 |
表3. 籼型酿酒专用水稻各指标主成分特征向量
在上述研究基础上,进一步明确直链淀粉含量分别与总淀粉、支链淀粉、垩白粒率、垩白度、整精米率、透明度的线性关系,以此推测满足酿酒水稻需求的籼型水稻的指标阈值。结果如图2所示。已知直链淀粉含量 ≥ 24%,带入各方程后,可得到各指标阈值见表4。
图2. 籼型酒用水稻酿酒适用性鉴定指标阈值拟合
水稻类型 | 直链淀粉含量 (%) | 总淀粉含量 (%) | 支链淀粉含量 (%) | 整精米率 (%) | 垩白粒率 (%) | 垩白度 (%) | 透明度 (%) |
---|---|---|---|---|---|---|---|
籼稻 | ≥24.00 | ≥81.46 | ≥58.39 | ≤81.83 | ≥72.88 | ≥18.91 | ≥47.98 |
粳稻 | ≥24.00 | ≤57.26 | ≤33.26 | ≥93.59 | ≤7.87 | - | ≥46.25 |
籼粳杂交稻 | ≥24.00 | ≤70.98 | ≤46.98 | ≥96.95 | ≥81.96 | ≥29.72 | ≤41.54 |
表4. 酿酒专用水稻评价指标阈值
在上述研究基础上,以直链淀粉含量 ≥ 24%为衡量标准,通过直链淀粉含量分别与总淀粉、支链淀粉、垩白粒率、垩白度、整精米率、透明度的线性关系,以此预测满足酿酒水稻需求的粳型、籼粳型水稻的指标阈值。结果如图3和图4所示。已知直链淀粉含量 ≥ 24%,带入各方程后,可得到粳型、籼粳型水稻的各指标阈值预测值见表4。
图3. 粳型酒用水稻酿酒适用性鉴定指标阈值预测
图4. 籼粳杂交型酒用水稻酿酒适用性鉴定指标阈值预测
一般食用稻米追求较高的直链淀粉,口感好,食味品质好;而酿酒用途的稻米追求较高的支链淀粉含量,再以特定工艺进行白酒酿造,提高稻米出酒率、优级率、白酒呈香呈味物质含量及品质,使酿酒专用粮的出酒率和优级率分别较普通粮食提高1.5%和6% [
本研究中根据统计学方法进行综合分析,发现籼型水稻的直链淀粉含量与支链淀粉、总淀粉含量、垩白粒率、垩白度间存在显著正相关,与整精米率间存在显著负相关;通过主成分分析,进一步筛选得到支链淀粉、总淀粉含量、垩白粒率、垩白度、整精米率、透明度共6个鉴定指标,并确定各指标阈值为:直链淀粉含量 ≥ 24.00%,总淀粉含量 ≥ 81.46%,支链淀粉含量 ≥ 58.39%,整精米率 ≤ 81.83%,垩白粒率 ≥ 72.8%,垩白度 ≥ 18.91%,透明度 ≥ 47.98%。研究结果与宜宾地方标准相符,且更为全面。
此外,粳稻直链淀粉含量与支链淀粉、总淀粉、脂肪含量呈显著负相关性,籼粳杂交稻直链淀粉含量与支链淀粉、总淀粉、脂肪含量、长宽比呈显著负相关性,与粒宽、垩白粒率、垩白大小、垩白度存在显著正相关性。由此可见,粳型和籼粳型稻米品质中约束直链淀粉含量的指标与籼型稻米并不相同。本研究尝试以地方标准DB5115/T 28-2020中的关键指标直链淀粉含量 ≥ 24.00%为基准,对总淀粉、支链淀粉含量、整精米率、垩白粒率、垩白度、透明度的阈值进行预测,结果见表4。
本研究结果不仅可作为地方标准的补充,也通过鉴定指标筛选,初步构建了酿酒专用水稻品种快速评价体系。结果可为酿酒专用水稻品种选育、筛选提供理论依据,改进目前酿酒专用粮基地建设中只盲目扩增种植面积、增产不提质的不足。同时,筛选指标简单易测,可实现现场速测,打破了酿酒企业水稻原料收购依赖经验、没有收购参考依据的局限,进一步提高稻米商用价值。研究结果在市场应用方面具有创新性。
本工作由固态发酵资源利用四川省重点实验室2020年度开放基金(2020GTJ009)支持。
罗润竹,金佩洋,黄 婷,陈泯英,张 国林,李 锐,戴海芳,武 辉. 不同类型水稻酒用特性对比及评价指标预测 Comparison of Brewing Characteristics of Different Types of Rice and Prediction of Evaluation Indexes[J]. 食品与营养科学, 2022, 11(02): 167-176. https://doi.org/10.12677/HJFNS.2022.112020