Brimrose AOTF-NIR光譜法測定各種混合物中%PMC，PVA和酪醇的含量
I.? 操作總結(jié)
本研究表明，使用Brimrose AOTF-NIR光譜儀確定這些化合物的各種混合物中的PVA，PMC和Casucol的百分比含量是可行的。使用通過光纖連接到Brimrose光譜儀的全束探頭收集數(shù)據(jù)。光譜儀將收集實時在線數(shù)據(jù)，每1-2秒1次。進一步的研究和校準將使Brimrose光譜儀能夠用于實時在線混合控制和工藝生產(chǎn)線中的實時產(chǎn)品控制。Brimrose的光譜儀非常堅固，沒有運動部件，對機械振動和環(huán)境變化不敏感，使其成為工業(yè)環(huán)境中在線過程控制的理想儀器設(shè)備。
II.簡介
聲光可調(diào)濾波器（AOTF）的原理基于光在各向異性介質(zhì)中的聲折射。裝置由粘在雙折射晶體上的壓電導層構(gòu)成。當導層被應(yīng)用的射頻（RF）信號激發(fā)時，在晶體內(nèi)產(chǎn)生聲波。傳導中的聲波產(chǎn)生折射率的周期性調(diào)制。這提供了一個移動的相柵，在特定條件下折射入射光束的部分。對于一個固定的聲頻，光頻的一個窄帶滿足相匹配條件，被累加折射。RF頻率改變，光的帶通中心相應(yīng)改變以維持相匹配條件。
光譜的近紅外范圍從800nm到2500 nm延伸。在這個區(qū)域最突出的吸收譜帶歸因于中紅外區(qū)域的基頻振動的泛頻和合頻。是基態(tài)到第二激發(fā)態(tài)或第三激發(fā)態(tài)的能級躍遷。因為較高能級躍遷連續(xù)產(chǎn)生的概率較小，每個泛頻的強度連續(xù)減弱。由于躍遷的第二或第三激發(fā)態(tài)所需的能量近似于第一級躍遷所需能量的二倍或三倍，吸收譜帶產(chǎn)生在基頻波長的一半和三分之一處。觸簡單的泛頻以外，也產(chǎn)生合頻。這些通常包括延伸加上一個或多個振動方式的伸縮。大量不同合頻是可能的，因而近紅外區(qū)域復(fù)雜，有許多譜帶彼此部分疊加。
現(xiàn)在，NIRS被用作定量工具，它依賴化學計量學來發(fā)展校正組成的參照分析和近紅外光譜的分析的關(guān)聯(lián)。近紅外數(shù)據(jù)的數(shù)學處理包括多元線性回歸法（MLR）、主成分分析法（PCA）、主成分回歸法（PCR）、偏最小二乘法（PLS）和識別分析。所有這些算法可以單獨或聯(lián)合使用來得到有價值組成的定性描述和定量預(yù)測。
III. 方法
- 數(shù)據(jù)收集
The samples consisted of a white powder contained in small vials and consisted of varying
percentages of the following constituents:
- PMC-50U (Cellulose ether)
- PVA (polyvinylalcohol)
- Casucol (starch ether)
There were 10 total pre-prepared samples.?? 7 samples had actual values were and the remaining 3 samples were classified as unknowns.? There were also 100 grams each of the pure form of the three constituents.? Additional samples were prepared using the pure form of each constituent.? A precision balance accurate to 0.1 mg was used to weigh the powder. Blending was done by vigorously shaking the mixture for approximately a minute.
All samples were scanned using the AOTF-NIR spectrometer with a full bundle transflectance probe.? The probe was placed in each sample such that its weight was the only force to compact the sample.? This minimized error in collecting data from the samples because the varying degree of compaction of particles plays a part in the scatter and reflection of light.? The spectral range was 1200nm to 2300nm with each spectrum consisting of an average of 200 scans.? The data were collected directly in the absorption mode.? The spectral data were entered into The Unscrambler? and PLS 1 regression analysis was performed on each of the constituents.
IV.?? Results
- Sample weights
| Samples Prepared by Brimrose | ||||||
| SAMPLE | Wt. PMC | Wt. PVA | Wt. Casucol | % PMC | % PVA | % Casucol |
| 1 | 4.9062 | 0.0580 | 0.0272 | 98.29 | 1.16 | 0.54 |
| 2 | 4.8098 | 0.0806 | 0.1050 | 96.28 | 1.16 | 2.10 |
| 3 | 4.7725 | 0.0278 | 0.2000 | 95.44 | 0.56 | 4.00 |
| 4 | 4.7344 | 0.1061 | 0.1744 | 94.41 | 2.12 | 3.48 |
| 5 | 4.6193 | 0.1520 | 0.2250 | 92.45 | 3.04 | 4.50 |
| 6 | 4.5562 | 0.2246 | 0.2501 | 90.56 | 4.46 | 4.97 |
| 7 | 4.4148 | 0.1256 | 0.4489 | 88.49 | 2.52 | 9.00 |
| 8 | 4.3308 | 0.3498 | 0.3004 | 86.95 | 7.02 | 6.03 |
| 9 | 4.2640 | 0.3995 | 0.3505 | 85.04 | 7.97 | 6.99 |
| 10 | 4.1985 | 0.4241 | 0.3741 | 84.03 | 8.49 | 7.49 |
| 11 | 4.1182 | 0.4755 | 0.3755 | 82.87 | 9.57 | 7.56 |
| 12 | 4.1035 | 0.4993 | 0.4005 | 82.02 | 9.98 | 8.00 |
| 13 | 4.0544 | 0.2005 | 0.7496 | 81.02 | 4.01 | 14.98 |
| 14 | 3.9940 | 0.2997 | 0.6999 | 79.98 | 6.00 | 14.02 |
| 15 | 3.9778 | 0.0249 | 1.0014 | 79.49 | 0.50 | 20.01 |
| 16 | 4.0343 | 0.625 | 0.9020 | 80.71 | 1.25 | 18.04 |
| 17 | 4.0537 | 0.1000 | 0.8503 | 81.01 | 2.00 | 16.99 |
| 18 | 4.0637 | 0.1374 | 0.8000 | 81.26 | 2.75 | 16.00 |
| 19 | 4.1260 | 0.2870 | 0.6000 | 82.31 | 5.73 | 11.97 |
| 20 | 4.6032 | 0.2497 | 0.1506 | 92.00 | 4.99 | 3.01 |
| Pre-prepared samples | ||||||
| B-1 | 79.45 | 0.5 | 20.0 | |||
| B-2 | 84.0 | 1.0 | 15.0 | |||
| B-3 | 88.0 | 2.0 | 10.0 | |||
| B-4 | 91.0 | 4.0 | 5.0 | |||
| B-5 | 91.0 | 6.0 | 3.0 | |||
| B-6 | 91.0 | 8.0 | 1.0 | |||
| B-7 | 89.5 | 10.0 | 0.5 | |||
Table 1.? Weights and percent values of all samples.
2.????? Spectra
Figure 2.?? Absorbance spectra of pre-prepared samples and samples prepared by Brimrose.
3. Regressions and Modeling
Figure 3.? PLS 1 regression model for % PMC.
Figure 4.? PLS 1 regression model for % PVA
Figure 5.? PLS 1 regression model for % Casucol.
The results for these regression models were excellent and showed good correlation between the calibration and validation sets.?? The regression for PMC had a SEC of 0.55 and an SEP of 0.93 with two outliers removed.? The regression for PVS had a SEC of 0.35 and an SEP of 0.61 with one outlier removed.?? The regression for Casucol had a SEC of 0.66 and an SEP of 0.98 with one outlier removed.
- Predictions
3 data points were removed from the data set and models were created using the remaining
data points.? The models were then used to predict the values for the points taken out and these values were compared to the known values.?? The results were excellent for such a small calibration set.? Predictions were then done for the 3 unknown samples.
| ??????????? CALCULATED PERCENT | ????????????? PREDICTED PERCENT | |||||
| SAMPLE | % PMC | %PVA | %CAS. | % PMC | %PVA | % CAS. |
| 6 | 90.56 | 4.46 | 4.97 | 90.22 | 5.07 | |
| 7 | 88.49 | 2.52 | 9.00 | 2.17 | ||
| 9 | 85.04 | 7.97 | 6.99 | 8.44 | ||
| 11 | 82.87 | 9.57 | 7.56 | 80.38 | 7.79 | |
| 20 | 92.00 | 4.99 | 3.01 | 1.17 | ||
| B-2 | 84.00 | 1.00 | 15.00 | 1.72 | ||
| B-4 | 91.00 | 4.00 | 5.00 | 91.13 | ||
Table 2.? Prediction of % values using models created with these samples removed.
| SAMPLE | % PMC | % PVA | % CASUCOL |
| T-1 | 77.62 | 5.06 | 17.79 |
| T-2 | 85.45 | 2.36 | 11.77 |
| T-3 | 97.92 | 0.64 | 1.44 |
Table 3.? ?Prediction of % values for the 3 unknown samples.
- Conclusions and Recommendations
It is concluded that it is feasible to use the Brimrose AOTF-NIR spectrometer to determine percent values for PMC, PVA, and Casucol.?? Both the regression models and values for the predictions show good correlation between the known values and the values determined from the spectral data.?? It is noted that the PVA and Casucol had larger error values than the PMC.? This is because the percent values for PVA and Casucol are much smaller than those for PMC.? The small number of data points used for this model definitely contributed to the error.? The results were excellent considering there were only 24 data points used in each model and past experience has shown that using 100 or more samples will certainly create a more robust model.? It is recommended that a purchase order be placed for a Brimrose AOTF-NIR spectrometer to allow for testing of a larger amount of samples which can be used to create a more robust model that can predict percent values for PMC, PVA, and Casucol using spectral data.
