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| 奇亚籽水分、脂肪、蛋白质和灰分的近红外 光谱快速检测模型优化 |
| Model optimization for determination of moisture, fat, protein and ash in chia seed by near infrared spectroscopy |
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| DOI：10.12166/j.zgyz.1003-7969/2020.07.029 |
| 中文关键词: 奇亚籽 近红外光谱 偏最小二乘法 定量模型 |
| 英文关键词:chia seed near infrared spectroscopy partial least-squares quantitative model |
| 基金项目:国家重点研发计划（2018YFC1602300） |
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| 中文摘要: |
| 水分、脂肪、蛋白质、灰分是重要的奇亚籽品质指标，目前主要依赖于化学法测定，过程烦琐，耗时费力，且不能多指标同时监测。以103份不同产地的奇亚籽为建模样本，通过19种光谱预处理方法和最佳谱区范围的筛选分别建立了奇亚籽中水分、脂肪、蛋白质、灰分的偏最小二乘模型。结果表明：对于水分采用Savitzky-Golay滤波平滑（SG）对光谱进行预处理，脂肪采用一阶导数（1st）和多元散射校正（MSC），蛋白质采用1st、标准正态变化（SNV）和SG，对于灰分采用1st、SNV和Norris微分平滑（ND）组合光谱进行预处理，针对各参数最佳预处理光谱采用人工法进行谱区筛选建立模型，得到水分、脂肪、蛋白质和灰分的验证集相关系数分别为0.993、0.972、0.925和0923。结果显示，利用近红外光谱可以实现对奇亚籽的水分、脂肪、蛋白质以及灰分的同时快速无损检测，在大规模奇亚籽原料的分选中提高检测效率。 |
| 英文摘要: |
| Determination of moisture, fat, protein and ash, four important quality indexes of chia seed, are based on chemical method, which is tedious, time-consuming, laborious, and can’t monitor multiple indexes simultaneously. 103 chia seed samples from various producing areas were collected. A total of 19 kinds of spectral pretreatment and selection of optimal spectral range, the partial least-squares models of moisture, fat, protein and ash in chia seed were established. The results showed that the spectra were processed by Savitzky-Golay filter (SG) for moisture; the combination of first derivative (1st) and multiplicative signal correction (MSC) was found to be the best preprocessing method for fat model; protein was pretreated with 1st, standard normal variate (SNV) and SG; for the detection of ash, 1st, SNV and Norris derivative filter (ND) spectral pretreatment method was better. Furthermore, new models were developed with the selected optimal pretreatments by artificial process. For moisture, fat, protein and ash, the correlation coefficients of prediction set were 0.993, 0.972, 0.925 and 0.923, respectively. Accordingly, near infrared spectroscopy could achieve the simultaneous, rapid and nondestructive detection of moisture, fat, protein and ash in chia seed to improve the detection efficiency for massive samples. |
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