WO2021046854A1 - 一种基于大数据的厨余垃圾分级方法 - Google Patents

一种基于大数据的厨余垃圾分级方法 Download PDF

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WO2021046854A1
WO2021046854A1 PCT/CN2019/105846 CN2019105846W WO2021046854A1 WO 2021046854 A1 WO2021046854 A1 WO 2021046854A1 CN 2019105846 W CN2019105846 W CN 2019105846W WO 2021046854 A1 WO2021046854 A1 WO 2021046854A1
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infrared
level
food waste
big data
samples
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PCT/CN2019/105846
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French (fr)
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袁增伟
张诗文
覃祖茂
盛虎
王婷
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南京大学（溧水）生态环境研究院
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light

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  • the invention belongs to the field of environment, and particularly relates to a method for grading food waste based on big data.
  • Food waste is the general term for the leftover meals discarded by the food and beverage units such as households, hotels, restaurants, etc. At present, about 40% of the most urban waste in the world is kitchen waste, mainly including rice and flour food residues, vegetables, vegetable oil, animal oil, meat bones, fish bones, etc. Its chemical components are mainly starch, cellulose, protein, lipids and inorganic salts.
  • Kitchen waste has the following characteristics: First, the content of organic matter such as crude protein and crude fiber is relatively high (each accounted for 16.73% and 2.52% of the dry food waste), which is of great development and utilization value, but it is perishable and produces foul smell; High water content (water mass fraction greater than 80%), inconvenient to collect and transport, low calorific value, improper handling is likely to produce secondary pollutants such as leachate; third is oil (crude fat accounts for 28.82% of the dry food waste ) And salt substances (NaCl content as high as 1.239%) are higher than other domestic wastes, which have a greater impact on the quality of resource-based products and require proper disposal.
  • the present invention provides a food waste classification method based on big data.
  • the present invention provides a method for grading food waste based on big data, establishes a near-infrared qualitative discrimination model for the main components of kitchen waste, and uses the established near-infrared qualitative discrimination model for the main components of kitchen waste to evaluate the kitchen waste.
  • Qualitative classification of remaining garbage specifically including:
  • the level is composed of first-level letters + first-level numbers + second-level letters + second-level numbers; the first-level letters are determined by the chemical components with the largest content.
  • the first-level number is determined by the percentage of the chemical component with the highest content;
  • the second-level letter is determined by the chemical component with the highest content, and the second-level number is determined by the percentage of the chemical component with the highest content;
  • the chemical components include: starch, denoted as A; cellulose, denoted as B; protein, denoted as C; lipids, denoted as D; inorganic salt, denoted as E.
  • the first-level and second-level numbers retain ten digits of percentage.
  • the method for determining the number of 100 infrared spectrograms choose two of the five samples at most, there are 10 selection methods, the most samples start from 100 to 10, and there are 10 types in total, so 100 samples are made.
  • the near-infrared spectrum acquisition conditions include detector, white light source, gain, moving mirror speed, scanning range, scanning times, and resolution index.
  • the near-infrared spectroscopy preprocessing refers to the near-infrared spectroscopy data of the modeling set sample collected in step (1), through smoothing processing, removal of trend correction, multiple scattering correction (MSC) and vector normalization (SNV) ) And other machine learning algorithms, and finally get the optimal conditions of spectrum preprocessing and wavelength range.
  • MSC multiple scattering correction
  • SNV vector normalization
  • the food waste identification and classification method provided by the present invention uses five common substances in kitchen waste as standard products, establishes a database of infrared spectra of 100 standard samples, and then uses a computer to compare the measured samples with the standard product spectra. , So as to classify the kitchen waste, which is convenient to guide the treatment of kitchen waste.
  • the level is composed of first-level letters + first-level numbers + second-level letters + second-level numbers; the first-level letters are determined by the chemical components with the largest content.
  • the first-level number is determined by the percentage of the chemical component with the highest content;
  • the second-level letter is determined by the chemical component with the highest content, and the second-level number is determined by the percentage of the chemical component with the highest content;
  • the chemical components include: starch, denoted as A; cellulose, denoted as B; protein, denoted as C; lipids, denoted as D; inorganic salt, denoted as E.
  • the first-level and second-level numbers retain ten digits of percentage.
  • the near-infrared spectrum acquisition conditions include detector, white light source, gain, moving mirror speed, scanning range, scanning times, and resolution index.
  • the near-infrared spectroscopy preprocessing refers to the near-infrared spectroscopy data of the modeling set sample collected in step (1) through smoothing processing, trend removal correction, multiple scattering correction (MSC), vector normalization (SNV) and other machines Learning algorithm, and finally get the optimal conditions of spectrum preprocessing and wavelength range.

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  • Spectroscopy & Molecular Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
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  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

一种基于大数据的厨余垃圾分级方法，包括：建立厨余垃圾主要组分的近红外定性判别模型，利用该模型对厨余垃圾定性分类。该方法利用厨余垃圾中常见的五种物质作为标准品，建立100个标准样品的红外图谱的数据库，再利用计算机对测定样品与标准品图谱进行比对，从而给厨余垃圾评级分类，便于指导厨余垃圾处理。

Description

一种基于大数据的厨余垃圾分级方法 技术领域
本发明属于环境领域，特别涉及一种基于大数据的厨余垃圾分级方法。
背景技术
厨余垃圾是家庭、宾馆、饭店等饮食单位抛弃的剩余饭菜的统称。目前世界各国绝大部分城市垃圾中约40％为厨余垃圾，主要包括米和面粉类食物残余、蔬菜、植物油、动物油、肉骨、鱼刺等。其化学组份主要为淀粉、纤维素、蛋白质、脂类和无机盐等。厨余垃圾有如下特点：一是粗蛋白和粗纤维等有机物含量较高(各占厨余垃圾干燥物的16.73％和2.52％)，开发利用价值较大，但易腐并产生恶臭；二是含水率高(水的质量分数大于80％)，不便收集运输，热值低，处理不当容易产生渗沥液等二次污染物；三是油类(粗脂肪占厨余垃圾干燥物的28.82％)和盐类物质(NaCl含量高达1.239％)含量较其它生活垃圾高，对资源化产品品质影响较大，需要妥善处理。
由于厨余垃圾中，含有的物质比较杂，不同物质含量不同，如果采用统一的处理方法，则处理效果不好，因此亟需一种基于大数据的厨余垃圾分级方法。
技术问题：为了解决现有技术的缺陷，本发明提供了一种基于大数据的厨余垃圾分级方法。
技术解决方案：本发明提供的一种基于大数据的厨余垃圾分级方法，建立厨余垃圾主要组分的近红外定性判别模型，利用建立厨余垃圾主要组分的近红外定性判别模型对厨余垃圾定性分类；具体包括：
(1)根据厨余垃圾主要化学组分含量差别，设定级别，级别由一级字母+一级数字+二级字母+二级数字组成；其中一级字母由含量最大的化学组份确定，一级数字由含量最大的化学组份百分比确定；二级字母由含量最大的化学组份确定，二级数字由含量最大的化学组份百分比确定；
(2)选取五种常见的淀粉类物质、纤维素类物质、蛋白质类物质、脂类物质 和无机盐类物质，根据步骤(1)的方法确定其分类编码，根据不同分类编码的物质含量比例以及该类物质在红外光谱的特征吸收峰，拟合物质含量-特征吸收峰的红外光谱图，共获得100份红外光谱图；
(3)准备近红外光谱仪和近红外光纤探头，对厨余垃圾样品进行实验室红外分析，并结合实验室分析结果，分别确定建模集样品的近红外光谱最优采集条件，将采集的样品红外光谱图与100份红外光谱图进行对比，图谱最接近的样本编号，则为待测样品编号，即为该样品分级。
作为改进，化学组份包括：淀粉，记为A；纤维素，记为B；蛋白质，记为C；脂类，记为D；无机盐，记为E。
作为改进，一级数字和二级数字保留百分比十位数字。
作为改进，100份红外光谱图的数量确定方法：五种样品最多的任选两个，则有10种选取方法，最多的样品从100开始到10，共有10种，故制得100份样本。
作为改进，所述近红外光谱采集条件包括检测器、白光光源、增益、动镜速度、扫描范围、扫描次数、分辨率指标。
作为改进，所述近红外光谱预处理是指将步骤(1)中采集的建模集样品近红外光谱数据，通过平滑处理、去除趋势校正、多元散射校正(MSC)以及矢量归一化(SNV)等机器学习算法，最终得到光谱预处理与波长范围的最优条件。
有益效果：本发明提供的厨余垃圾鉴定分类方法利用厨余垃圾中常见的五种物质作为标准品，建立100标准样品的红外图谱的数据库，再利用计算机对测定样品与标准品图谱进行比对，从而给厨余垃圾评级分类，便于指导厨余垃圾处理。
发明实施例
本发明的实施方式
下面结合实施例，对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明，但不用来限制本发明的范围。
基于大数据的厨余垃圾分级方法，建立厨余垃圾主要组分的近红外定性判别模型，利用建立厨余垃圾主要组分的近红外定性判别模型对厨余垃圾定性分类；具体包括：
(1)根据厨余垃圾主要化学组分含量差别，设定级别，级别由一级字母+一级数字+二级字母+二级数字组成；其中一级字母由含量最大的化学组份确定，一级数字由含量最大的化学组份百分比确定；二级字母由含量最大的化学组份确定，二级数字由含量最大的化学组份百分比确定；
化学组份包括：淀粉，记为A；纤维素，记为B；蛋白质，记为C；脂类，记为D；无机盐，记为E。
一级数字和二级数字保留百分比十位数字。
(2)选取五种常见的淀粉类物质、纤维素类物质、蛋白质类物质、脂类物质和无机盐类物质，根据步骤(1)的方法确定其分类编码，根据不同分类编码的物质含量比例以及该类物质在红外光谱的特征吸收峰，拟合物质含量-特征吸收峰的红外光谱图，共获得100份红外光谱图；
100份红外光谱图的数量确定方法：五种样品最多的任选两个，则有10种选取方法，最多的样品从100开始到10，共有10种，故制得100份样本。
(3)准备近红外光谱仪和近红外光纤探头，对厨余垃圾样品进行实验室红外分析，并结合实验室分析结果，分别确定建模集样品的近红外光谱最优采集条件，将采集的样品红外光谱图与100份红外光谱图进行对比，图谱最接近的样本编号，则为待测样品编号，即为该样品分级。
所述近红外光谱采集条件包括检测器、白光光源、增益、动镜速度、扫描范围、扫描次数、分辨率指标。
所述近红外光谱预处理是指将步骤(1)中采集的建模集样品近红外光谱数据，通过平滑处理、去除趋势校正、多元散射校正(MSC)以及矢量归一化(SNV)等机器学习算法，最终得到光谱预处理与波长范围的最优条件。
以上所述实施例仅表达了本发明的若干实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。

Claims (6)

  1. 一种基于大数据的厨余垃圾分级方法，其特征在于：建立厨余垃圾主要组分的近红外定性判别模型，利用建立厨余垃圾主要组分的近红外定性判别模型对厨余垃圾定性分类；具体包括：
    (1)根据厨余垃圾主要化学组分含量差别，设定级别，级别由一级字母+一级数字+二级字母+二级数字组成；其中一级字母由含量最大的化学组份确定，一级数字由含量最大的化学组份百分比确定；二级字母由含量最大的化学组份确定，二级数字由含量最大的化学组份百分比确定；
    (2)选取五种常见的淀粉类物质、纤维素类物质、蛋白质类物质、脂类物质和无机盐类物质，根据步骤(1)的方法确定其分类编码，根据不同分类编码的物质含量比例以及该类物质在红外光谱的特征吸收峰，拟合物质含量-特征吸收峰的红外光谱图，共获得100份红外光谱图；
    (3)准备近红外光谱仪和近红外光纤探头，对厨余垃圾样品进行实验室红外分析，并结合实验室分析结果，分别确定建模集样品的近红外光谱最优采集条件，将采集的样品红外光谱图与100份红外光谱图进行对比，图谱最接近的样本编号，则为待测样品编号，即为该样品分级。
  2. 根据权利要求1所述的一种基于大数据的厨余垃圾分级方法，其特征在于：化学组份包括：淀粉，记为A；纤维素，记为B；蛋白质，记为C；脂类，记为D；无机盐，记为E。
  3. 根据权利要求1所述的一种基于大数据的厨余垃圾分级方法，其特征在于：一级数字和二级数字保留百分比十位数字。
  4. 根据权利要求1所述的一种基于大数据的厨余垃圾分级方法，其特征在于：100份红外光谱图的数量确定方法：五种样品最多的任选两个，则有10种选取方法，最多的样品从100开始到10，共有10种，故制得100份样本。
  5. 根据权利要求1所述的一种基于大数据的厨余垃圾分级方法，其特征在于：所述近红外光谱采集条件包括检测器、白光光源、增益、动镜速度、扫描范围、扫描次数、分辨率指标。
  6. 根据权利要求1所述的一种基于大数据的厨余垃圾分级方法，其特征在于：所述近红外光谱预处理是指将步骤(1)中采集的建模集样品近红外光谱数据，通过平滑处理、去除趋势校正、多元散射校正(MSC)以及矢量归一化(SNV)等机器学习算法，最终得到光谱预处理与波长范围的最优条件。
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CN106198423A (zh) * 2016-09-12 2016-12-07 电子科技大学 一种基于可见‑近红外光谱分析技术鉴别火腿肠等级的方法
CN108982409A (zh) * 2018-08-08 2018-12-11 浙江工业大学 一种基于近红外光谱快速检测大型褐藻木质纤维素三组分含量的方法
CN109211835A (zh) * 2018-10-11 2019-01-15 南京大学（溧水）生态环境研究院 一种基于光谱技术的厨余垃圾快速鉴定方法

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