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采用频率为100Hz、占空比为50%的方波信号对近红外发光二极管(light-emitting diode,LED)进行脉冲宽度调制(pulse width modulation,PWM),调制后的光强信号与样品相互作用后,携带浊度大小信息的透射光信号和90°方向的散射光信号被光电探测器接收并将其转换为电信号,再经电路放大滤波后进行频谱分析。
根据朗伯-比尔定律,透射光强和浊度的关系可以表示为:
$ {I_1} = {I_0}{\rm{exp}}( - {\tau _1}l) $
(1) 式中,I1为透射光强,I0为入射光强,l为光穿过样品的长度,τ1为朗伯-比尔定律下的样品浊度系数。经过光电转换和电路的放大处理后得到的电信号为:
$ {S_1} = {K_1}{I_1} = {K_1}{I_0}{\rm{exp}}( - {\tau _1}l) $
(2) 式中, K1为电路增益系数。
散射光信号遵循米散射理论,同样经过光电转换和电信号放大得到与浊度的关系式为:
$ {S_2} = {K_2}{I_2} = {K_2}M{\tau _2}{I_0} $
(3) 式中,K2为电路增益系数,I2为散射光强,M为米散射系数,τ2为米散射理论下的样品浊度系数。
传感器利用比值法,将散射光强和透射光强取对数之后的值相比:
$ S = \frac{{{S_2}}}{{\ln {S_1}}} = \frac{{{K_2}M{\tau _2}{I_0}}}{{\ln \left[ {{K_1}{I_0}\exp \left( { - {\tau _1}l} \right)} \right]}} = k\tau + b $
(4) 式中,k和b为常系数,τ为比值后样品浊度系数。由(4)式可知:比值与浊度具有线性关系。利用比值法可以消除样品颜色和环境光等的干扰,从而提高传感器的测量精度。
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频谱分析是将脉冲调制后的透射光和散射光的时域信号通过快速傅里叶变换(fast Fourier transformation,FFT)转换到频域进行分析和处理的一种方法,在频域中可以得到相对于时域更加丰富的特征量[15]。
频率为1/T、占空比为50%的方波信号,其时域表达式为:
$ f(t) = \left\{ {\begin{array}{*{20}{l}} {0, (0 < t < T/2)}\\ {A, (T/2 < t < T)} \end{array}} \right. $
(5) 式中,t为时间,A为幅值。根据傅里叶级数,方波的傅里叶级数的偶次谐波分量均为0,因此方波可以表示为一系列奇次谐波分量的叠加:
$ \begin{array}{l} f(t) = \frac{4}{{\rm{ \mathsf{ π} }}}A\left[ {\sin (\omega t) + \frac{1}{3}\sin (3\omega t) + \frac{1}{5}\sin (5\omega t) + } \right.\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\left. {\frac{1}{7}\sin (7\omega t) + \cdots } \right] = \\ \;\;\;\;\;\;\;\;\frac{4}{{\rm{ \mathsf{ π} }}}A\sum\limits_{n = 1}^\infty {\frac{1}{{2n - 1}}} \sin [(2n - 1)\omega t] \end{array} $
(6) 式中,ω为谐波频率。进行n点FFT,变换后的每个点可以用复数表示为:α+βi。
根据FFT原理[17],变换后第n点对应时域中一定频率的正弦信号:
$ {f_n}(t) = {A_n}\cos \left( {2{\rm{ \mathsf{ π} }}{F_n}t + {\varphi _n}} \right) $
(7) 式中,An, Fn, φn分别为n点信号的幅值、频率和相位角。
经电路放大滤波后的时域调制信号,再由模数转换为数字信号,在单片机内部利用FFT变换进行频谱分析,将调制信号在频域的奇次谐波分量提取出来,得到和浊度相关性强的谐波分量(基频、三次谐波和五次谐波)。
由于时域中透射光强和散射光强的变化对应频域中其各次谐波分量幅值的变化,因此,可以在频域中构建谐波幅值与浊度的数学模型,同(4)式。
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根据ISO7027国际标准[22],采用福尔马肼标准浊度液进行传感器的标定。根据稀释定律:
$ {V_1} = \frac{{{N_2} \times {V_2}}}{{{N_1}}} $
(8) 式中, N2为稀释后的浊度,V2为稀释后的体积,N1为原液浊度,V1为所需原液量。用0NTU的双蒸水将1000NTU的标准浊液梯度稀释得到16种不同浊度的样液,如表 1所示。分别将这16组样液逐次加入到传感器的比色皿中,用示波器同时采集放大滤波后的透射和散射电压信号,将数据上传到计算机中,以便进行分析。图 5为各个浊度下透射和散射的原始时域信号图。透射信号的幅值随样品浊度的增加而衰减,散射信号的幅值随浊度的增加而增大。为提高信号的信噪比,通过db4的小波滤波算法滤除噪声[16],图 6为滤波后的时域响应信号。
Table 1. Samples of different turbidity
sample number turbidity/NTU sample number turbidity/NTU 1 0 9 300 2 10 10 400 3 20 11 500 4 30 12 600 5 40 13 700 6 50 14 800 7 100 15 900 8 200 16 1000 利用FFT变换将滤波后的信号进行频谱转换,得到不同浊度下对应的幅频关系图,如图 7所示。频域信号的一次谐波、三次谐波和五次谐波的幅值随浊度增加的变化最为明显,如课题组前期工作[15-16]。将透射信号一次谐波、三次谐波、五次谐波的幅值取对数,与对应频率下散射信号的幅值进行比值,得到浊度与比值的散点图,如图 8所示。其中,图 8a为100Hz比值与浊度关系图,可以看出其不成线性关系; 图 8b为300Hz和500Hz的拟合结果。浊度和比值呈线性相关,拟合关系式为(9)式和(10)式,相关系数R分别为0.9883和0.9946,在500Hz下具有更好的线性度。
$ {y_{300Hz}} = - 0.4412 - 2.74 \times {10^{ - 4}}x $
(9) $ {y_{500Hz}} = - 0.0098 - 8.85 \times {10^{ - 5}}x $
(10) -
将500Hz下的线性拟合关系式移植到嵌入式平台实现最终的浊度值计算。为验证标定结果的可靠性,重新配置了8种不同浊度的标准浊度液进行验证性测量,结果如表 2所示。其中平均相对误差如下:在70NTU时,最小测量误差为0.471%;在540NTU时,最大测量误差为3.768%。
Table 2. Measurement results of standard turbidity liquid
sample number sample turbidity/NTU mean of measurement/NTU average error/% 1 0 0.93 — 2 70 70.33 0.471 3 140 141.08 0.771 4 280 289.36 3.343 5 360 363.18 0.883 6 460 465.62 1.222 7 540 560.345 3.768 8 640 660.045 3.132 9 740 764.415 3.299 10 840 860.19 2.404 11 1000 1015.39 1.539
基于频谱分析的中药浊度在线传感器研究
Research on an on-line turbidity sensor for traditional Chinese medicine based on spectrum analysis
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摘要: 为了实现中药浊度在线精确测量,采用频谱分析法,将样品透射光和散射光信号的频谱特征与浊度之间建立联系,研究了一种低成本中药浊度在线传感器。将近红外发光二极管作为传感器的光源,通过FDS100光电二极管进行光电信号转换,经跨阻放大器和低通滤波器处理后由模数转换器转为数字信号,采用STM32F405单片机进行快速傅里叶变换和浊度反演;应用梯度稀释法制备了16组0NTU~1000NTU的福尔马肼标准浊度液对传感器做标定与验证实验,并测试了当归精油浊度的线性度。结果表明,透射和散射信号的3次谐波、5次谐波分量的幅值比值与浊度的线性拟合相关系数各为0.9883和0.9946;传感器的最小误差为0.471%,最大误差为3.768%;当归精油浊度的线性拟合度为0.99176,达到了精油提取、浓缩及干燥的实时在线测量需求。该传感器在中药的制造加工和质量监测中具有一定的应用价值。Abstract: In order to realize the on-line accurate measurement of turbidity of traditional Chinese medicine (TCM), the spectrum characteristics analysis method was used to establish the relationship between the spectrum characteristics and turbidity of the transmitted and scattered light signals. A near-infrared LED was employed as the light source of the sensor. The transmitted light and scattered light signals were converted into weak current signals by FDS100 photodiodes, then conditioned by a trans-impedance amplifier and a low-pass filter. The processed signals then were converted by A/D converters to digital signals. A MCU (STM32F405) was used to calculate fast Fourier transformation and turbidity. Finally, sixteen groups of standard turbidity solution (Formazin) ranging from 0NTU to 1000NTU were prepared using gradient dilution method to calibrate our sensor and make further verification, in addition, the sensor was applied for determination of the turbidity of angelica essential oil. The results show that the related coefficients of linear fitting of a ratio of the third and fifth harmonics components of the transmitted signal amplitude and the scattered signal amplitude are 0.9883 and 0.9946, respectively. And the minimum error and maximum error of our sensor are 0.471% and 3.768%, respectively. A good linear fitting degree of the turbidity of angelica essential oil is 0.99176, which satisfies real-time and on-line measurement requirements of essential oil extraction, concentration and drying. Therefore, the sensor has certain application filed for manufacturing and quality monitoring of TCM.
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Table 1. Samples of different turbidity
sample number turbidity/NTU sample number turbidity/NTU 1 0 9 300 2 10 10 400 3 20 11 500 4 30 12 600 5 40 13 700 6 50 14 800 7 100 15 900 8 200 16 1000 Table 2. Measurement results of standard turbidity liquid
sample number sample turbidity/NTU mean of measurement/NTU average error/% 1 0 0.93 — 2 70 70.33 0.471 3 140 141.08 0.771 4 280 289.36 3.343 5 360 363.18 0.883 6 460 465.62 1.222 7 540 560.345 3.768 8 640 660.045 3.132 9 740 764.415 3.299 10 840 860.19 2.404 11 1000 1015.39 1.539 -
[1] WEI X Y, LIU Q, LI R, et al. Discussion on pharmacological understanding and inheritance of ancient Chinese medicines[J]. Shanghai Journal of Traditional Chinese Medicine, 2020, 54(5): 73-76(in Chinese). [2] CHANG Y, YU F J, HU Ch L. Discussion on the test of clarity and degree of opalescence of liquids in Ch. P 2015[J]. Chinese Pharmaceutical Journal, 2017, 52(9): 802-808(in Chinese). [3] IRVINE C A, BACKUS S, COOKE S, et al. Application of continuous turbidity sensors to supplement estimates of total phosphorus concentrations in the Grand River, Ontario, Canada[J]. Journal of Great Lakes Research, 2019, 45(4): 840-849. doi: 10.1016/j.jglr.2019.05.007 [4] WU Y H, WU H, HUANG H D, et al. Study on influuencial factor for detecting the concentration of HPAM by turbidimetry[J]. Journal of Oil and GAS Technology, 2009, 31(4): 139-142(in Chinese). [5] LIANG J H, WEN D J, LI M F, et al. Study on the determination of phosphorus content in soybean hair oil by turbidity method[J]. Cereal & Food Industry, 2019, 26(3): 70-72(in Chinese). [6] YU J Q, XU B, HUANG Y Y, et al. Evaluation and classification of dissolution behavior and capability of Chinese medicine granules based on an inline turbidity sensor[J]. China Journal of Chinese Materia Medica, 2020, 45(2): 259-266(in Chinese). [7] EBERT F V, REITZ C, CRUZ-BOURNAZOU M N, et al. Characterization of a noninvasive on-line turbidity sensor in shake flasks for biomass measurements[J]. Biochemical Engineering Journal, 2018, 132: 20-28. doi: 10.1016/j.bej.2018.01.001 [8] LIU Y N, DUAN Q H, ZHANG Y. Study on metal detergent turbidity measurement by scattered light turbidimeter[J]. Petroleum Processing and Petrochemicals, 2017, 48(2): 100-105(in Chinese). [9] PARRA L, ROCHER J, ESCRIVÁ J, et al. Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms[J]. Aquacultural Engineering, 2018, 81: 10-18. doi: 10.1016/j.aquaeng.2018.01.004 [10] PAUL W, ALAN T, COLIN P, et al. Encyclopedia of analytical science[M]. 3rd ed. Amsterdam, Amsterdam, Netherlands: Elsevier, 2019: 152-163. [11] GILLETT D, MARCHIORI A. A low-cost continuous turbidity monitor[J]. Sensors, 2019, 19(14): 3039. doi: 10.3390/s19143039 [12] YEOH S, MATJAFRI M Z, MUTTER K N, et al. Plastic fiber evanescent sensor in measurement of turbidity[J]. Sensors & Actuators, 2019, A285: 1-7. [13] HU Y, SUN L, YE S, et al. A highly sensitive in-situ turbidity sensor with low power consumption[J]. Photonic Sensors, 2014, 4(1): 77-85. doi: 10.1007/s13320-013-0154-z [14] QI Sh B, YI B A, YU J D. Turbidity detection system based on scattering and transmission method[J]. Mechanical & Electrical Engineering Magazine, 2019, 36(8): 771-776(in Chinese). [15] ZANG Zh Zh, QIU X B, GUAN Y M, et al. Determining moisture content of traditional Chinese medicines using a near-infrared LED-based moisture content sensor with spectrum analysis[J]. Optical and Quantum Electronics, 2019, 51(5): 133. doi: 10.1007/s11082-019-1848-2 [16] ZANG Zh Zh, QIU X B, GUAN Y M, et al. A novel low-cost turbidity sensor for in-situ extraction in TCM using spectral components of transmitted and scattered light[J]. Measurement, 2020, 160: 107838. doi: 10.1016/j.measurement.2020.107838 [17] ZHU L K, JIA F X, LI X L. Design of parallel high-speed FFT algorithm based on laser seeker signal[J]. Laser Technology, 2018, 42(1): 89-93(in Chinese). [18] BAI J W, ZHANG D Y, LIU Ch. Research of turbidity measuring influence caused by two different light source[J]. Optical Instruments, 2008, 30(2): 1-3(in Chinese). [19] LI N, QIU X B, WEI Y B, et al. A portable low-power integrated current and temperature laser controller for high-sensitivity gas sensor applications[J]. Review of Scientific Instruments, 2018, 89(10): 103103. doi: 10.1063/1.5044230 [20] QIU X B, WEI Y B, SUN D Y, et al. A miniaturized laser mea-surement instrument of ammonia escaping from coal-fired power plants[J]. Laser Technology, 2019, 43(5): 697-701(in Chin-ese). [21] QIU X B. Design and application of embedded photoelectric detection system[M]. Beijing: Publishing House of Electronics Industry, 2019: 76-81(in Chinese). [22] INTERNATIONAL ORGANIZATION FOR STANDARDIZATION. ISO 7027-1: 2016. Water quality-determination of turbidity[S]. Geneva, Switzerland: The Spanish Association for Standardization and Certification, 2016: 1-7.