{"id":261,"date":"2019-08-14T17:21:38","date_gmt":"2019-08-14T09:21:38","guid":{"rendered":"https:\/\/www.bihec.com\/brimrose\/?p=261"},"modified":"2019-08-15T10:51:37","modified_gmt":"2019-08-15T02:51:37","slug":"brimrose-aotf-nir%e8%bf%91%e7%ba%a2%e5%a4%96%e5%85%89%e8%b0%b1%e6%b3%95%e6%b5%8b%e5%ae%9a%e7%ba%a4%e7%bb%b4%e4%b8%ad%e5%b0%bc%e9%be%9966%ef%bc%8c%e8%81%9a%e4%b8%99%e7%83%af%e5%92%8c%e4%b8%81%e8%8b%af","status":"publish","type":"post","link":"https:\/\/www.bihec.com\/brimrose\/brimrose-aotf-nir%e8%bf%91%e7%ba%a2%e5%a4%96%e5%85%89%e8%b0%b1%e6%b3%95%e6%b5%8b%e5%ae%9a%e7%ba%a4%e7%bb%b4%e4%b8%ad%e5%b0%bc%e9%be%9966%ef%bc%8c%e8%81%9a%e4%b8%99%e7%83%af%e5%92%8c%e4%b8%81%e8%8b%af\/","title":{"rendered":"Brimrose AOTF-NIR\u8fd1\u7ea2\u5916\u5149\u8c31\u6cd5\u6d4b\u5b9a\u7ea4\u7ef4\u4e2d\u5c3c\u9f9966\uff0c\u805a\u4e19\u70ef\u548c\u4e01\u82ef\uff0c\u78b3\u9178\u9499\u548c\u4e09\u6c34\u5408\u7269"},"content":{"rendered":"

\u6d4b\u5b9a\u603b\u7ed3<\/u><\/strong><\/p>\n

Brimrose<\/a> ATOF-NIR<\/a>\u81ea\u7531\u7a7a\u95f4\u5149\u8c31\u4eea<\/a>\u7528\u4e8e\u626b\u63cf\u542b\u6709\u4e0d\u540c\u91cf \u5c3c\u9f9966<\/a>\uff0c \u805a\u4e19\u70ef<\/a>\u548c \u4e01\u82ef<\/a>\uff0c \u78b3\u9178\u9499<\/a>\u548c \u4e09\u6c34\u5408\u7269<\/a>\u7684 \u7ea4\u7ef4<\/a>\u6837\u54c1\uff0c\u63d0\u4f9b\u4e865\u4e2a\u6837\u672c\uff0c\u6bcf\u4e2a\u6837\u672c\u626b\u63cf5\u6b21\u3002\u6837\u54c1\u7684\u5927\u5c0f\u548c\u8d28\u5730\u5404\u4e0d\u76f8\u540c\uff0c\u4ece\u7ec6\u7c89\u5230\u5c0f\u5757\u7ea4\u7ef4\uff0c\u518d\u5230\u8f83\u5927\u7684\u5730\u6bef\u7ea4\u7ef4\u3002\u539f\u59cb\u5149\u8c31\u6570\u636e\u7ecf\u8fc7\u5904\u7406\u540e\u4e3a\u5438\u6536\u548c\u7b2c\u4e00\u5bfc\u6570\u3002\u5438\u5149\u5ea6\u548c\u7b2c\u4e00\u4e2a\u5bfc\u6570\u5149\u8c31\u88ab\u5bfc\u5165\u5230\u5316\u5b66\u6d4b\u91cf\u8f6f\u4ef6\u4e2d\uff0c\u521b\u5efa\u4e86PLS 1\u56de\u5f52\u6a21\u578b\uff0c\u5c06\u5149\u8c31\u6570\u636e\u4e0e\u5c3c\u9f996.6\uff0c\u805a\u4e19\u70ef\u548c\u4e01\u82ef\uff0c\u78b3\u9178\u9499\u548c\u4e09\u6c34\u5408\u7269\u7684\u767e\u5206\u6bd4\u542b\u91cf\u76f8\u5173\u8054\u3002\u8fd9\u4e2a\u6a21\u578b\u663e\u793a\uff0c\u5149\u8c31\u6570\u636e\u4e0e\u53c2\u8003\u503c\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u826f\u597d\u3002<\/p>\n

\u626b\u63cf\u4e865\u4e2a\u6837\u672c\uff0c\u4f46\u4ec5\u4f7f\u75284\u4e2a\u6837\u672c\u8fdb\u884c\u6821\u51c6\uff0c\u56e0\u4e3a\u5bf9\u4e00\u4e2a\u6837\u672c\u7684\u53c2\u8003\u503c\u5b58\u5728\u7591\u95ee\u3002\u6a21\u578b\u7684\u8f7d\u8377\u91cd\u91cf\u8868\u660e\uff0c\u6a21\u578b\u7684\u76f8\u5173\u4fe1\u606f\u53d6\u81ea1700nm\u5de6\u53f3\u7684C-H\u5438\u6536\u533a\u57df\u548c2050nm\u5de6\u53f3\u7684C-O\u5438\u6536\u533a\uff0c\u8fd9\u4e9b\u662f\u5728\u6d4b\u91cf\u8fd9\u4e9b\u53c2\u6570\u65f6\uff0c\u4eba\u4eec\u671f\u671b\u80fd\u83b7\u53d6\u4fe1\u606f\u7684\u6ce2\u957f\u533a\u57df\u3002\u8003\u8651\u5230\u6821\u51c6\u53ea\u4f7f\u7528\u4e86\u56db\u4e2a\u6837\u54c1\uff0c\u5e76\u4e14\u6240\u4f7f\u7528\u7684 \u5149\u8c31\u4eea<\/a>\u5e76\u6ca1\u6709\u9488\u5bf9\u6b64 \u5e94\u7528<\/a>\u8fdb\u884c\u4f18\u5316\uff0c\u56e0\u6b64\u6d4b\u8bd5\u7ed3\u679c\u5e94\u8be5\u8bf4\u7279\u522b\u597d\u3002\u672c\u7814\u7a76\u7684\u7ed3\u679c\u57fa\u4e8e\uff0c\u5f53\u53ef\u4ee5\u4f7f\u7528\u66f4\u591a\u6837\u672c\uff0c\u4f7f\u7528\u626b\u63cf\u6536\u96c6\u5149\u8c31\u6570\u636e\u65f6\u4e0e\u6837\u54c1\u63a5\u89e6\u7684\u624b\u6301\u5149\u8c31\u4eea\u3002<\/p>\n

\u603b\u7684\u6765\u8bf4\uff0c\u672c\u7814\u7a76\u8bc1\u660e\u4e86\u4f7f\u7528Brimrose AOTF-NIR<\/a>\u8fd1\u7ea2\u5916\u5149\u8c31\u4eea<\/a>\u6536\u96c6\u7684\u5149\u8c31\u6570\u636e\u6d4b\u91cf\u5c3c\u9f9966\uff0c\u805a\u4e19\u70ef\u548c\u4e01\u82ef\uff0c\u78b3\u9178\u9499\u548c\u4e09\u6c34\u5408\u7269\u7684\u53ef\u884c\u6027\u3002<\/p>\n

\u603b\u7ed3\u4ecb\u7ecd<\/u><\/strong><\/p>\n

AOTF<\/a>\u7684\u539f\u7406\u662f\u57fa\u4e8e\u5149\u5728\u5404\u5411\u540c\u6027\u4ecb\u8d28\u4e2d\u7684\u58f0\u884d\u5c04\uff0c\u8be5\u88c5\u7f6e\u7531\u4e00\u4e2a\u538b\u7535\u4f20\u611f\u5668\u4e0e\u4e00\u4e2a\u53cc\u6298\u5c04\u4eea\u8fde\u63a5\u5728\u4e00\u8d77\u6784\u6210\uff0c\u5f53\u4f20\u611f\u5668\u88ab\u5e94\u7528\u7684\u5c04\u9891\u4fe1\u53f7\u6fc0\u6d3b\u65f6\uff0c\u4f1a\u5728\u6676\u4f53\u4e2d\u4ea7\u751f\u58f0\u6ce2\u3002\u4f20\u64ad\u58f0\u6ce2\u4ea7\u751f\u4e86\u6298\u5c04\u7387\u7684\u5468\u671f\u6027\u8c03\u5236\uff0c\u8fd9\u63d0\u4f9b\u4e86\u4e00\u4e2a\u79fb\u52a8\u5149\u6805\uff0c\u5728\u9002\u5f53\u7684\u6761\u4ef6\u4e0b\uff0c\u4f1a\u884d\u5c04\u90e8\u5206\u5165\u5c04\u5149\u675f\u3002\u5bf9\u4e8e\u56fa\u5b9a\u58f0\u9891\uff0c\u7a84\u9891\u6bb5\u7684\u5149\u9891\u6ee1\u8db3\u5339\u914d\u6761\u4ef6\uff0c\u5e76\u7d2f\u79ef\u884d\u5c04\u3002\u968f\u7740RF\u9891\u7387\u7684\u53d8\u5316\uff0c\u5149\u5e26\u7684\u4e2d\u5fc3\u4e5f\u4f1a\u76f8\u5e94\u5730\u6539\u53d8\uff0c\u4ece\u800c\u4fdd\u6301\u76f8\u4f4d\u5339\u914d\u6761\u4ef6\u3002<\/p>\n

$\"\"$ <\/p>\n

The near infrared region of the spectrum extends from 800nm to 2500nm.\u00a0 The absorption bands that are most prominent in this region are due to overtones and combinations of the fundamental vibrations active in the mid-infrared region.\u00a0 The energy transitions are<\/p>\n

between the ground state and the second or third excited vibrational states.\u00a0 Because higher energy transitions are successively less likely to occur, each overtone is successively weaker in intensity.\u00a0 Because the energy required to reach the second or third excited state is approximately twice or three times that needed for a first order transition and the wavelength of absorption is inversely proportional to the energy, the absorption bands occur at about one-half and one-third the wavelength of the fundamental.\u00a0 In addition to the simple overtones, combination bands also occur.\u00a0 These usually involve a stretch plus one or more bending of rocking modes.\u00a0 Many different combinations are possible and therefore the NIR region is complex, with many band assignments unresolved.<\/p>\n

Near Infrared Spectroscopy is currently being used as a quantitative tool that relies on chemometrics to develop calibrations relating a reference analysis of the constituent to that of the NIR optical spectrum.\u00a0 The mathematical treatment of NIR data includes Multi Linear Regression (MLR), Principle Component Analysis (PCR), Partial Least Squares (PLS) and discriminant analysis.\u00a0 All of these algorithms can be used singularly or in combination to yield the resultant goal of quantitative prediction and qualitative description of the constituents of interest.<\/p>\n

III.\u00a0 Methodology<\/u><\/strong><\/p>\n

A Brimrose AOTF-NIR Free Space spectrometer was used to scan five samples with containing varying amounts of Nylon 6,6, Polypropylene & Styrene Butadiene, and Calcium Carbonate & Aluminum Tri-Hydrate.\u00a0\u00a0 Five different portions of each sample were scanned.\u00a0 Wavelength range was from 1100nm to 2300nm with 2nm resolution.\u00a0 One hundred scans were collected per reading and averaged into one spectrum.\u00a0 The raw spectral data were post-processed into absorbance and first derivative.\u00a0 The absorbance and first derivative spectra were imported into the chemometrics software program The Unscrambler.\u00a0 The first derivative spectra were used to create PLS 1 regression models for N66, PP & SB, and CC & AlTH.\u00a0 It was noted that the reference values for Sample 2 may have been erroneous.\u00a0 When the samples were shipped, nominal values for the parameters were provided.\u00a0 Sample 2 had an estimated value of 38%-42% for N66, 38%-42% for PP, and 20%-24% for CC & AlTH.\u00a0 When the actual sample values were provided for Sample 2, N66 had a value of 62.6%, PP & SB had a value of 25.4%, and CC & AlTH had a value of 12.0%.\u00a0 The reasons for the discrepancies were unclear but the models for all three parameters showed better results when the data points from Sample 2 were removed.\u00a0 One possible reason is that Sample 2 was made up of much larger pieces of fiber than the other samples.\u00a0 The variance of reference values within the given sample may have been much higher than the variance in the other samples.\u00a0 Thus, all models shown here do not have data points from Sample 2<\/p>\n

Results<\/strong><\/u><\/p>\n

\n
Spectra<\/strong><\/li>\n<\/ol>\n
$\"\"$ <\/p>\n
There is a clear baseline shift effect in the spectra due to differences in the distance of the samples from the optical head of the Free Space spectrometer.\u00a0 Chemometric modeling handles baseline shifts well and a hand-held spectrometer that is in contact with the samples when scanning will greatly reduce this effect.\u00a0 The following graph shows the first derivative spectra of the samples.<\/p>\n
$\"\"$ <\/p>\n
The first derivative spectra eliminate the baseline shift effect and make the spectral differences between the samples much clearer.\u00a0 The differences between Sample 5 and the rest of the samples are especially clear.\u00a0 The Sample 5 spectra are shown in red on the graph.\u00a0 This sample contains a much higher % CC & AlTH and lower %N66 and %PP & SB than the other samples.\u00a0 The differences are most clear around 1700nm, 2050nm, and 2250nm.\u00a0 The regression models will show that these wavelength areas are important areas in the models.\u00a0 The first derivative spectra were used to create regression models for N66, PP & SB, and CC & AlTH.\u00a0 Modeling results are shown in the graphs below.<\/p>\n
<\/p>\n
\n
Modeling and Regressions<\/strong><\/li>\n<\/ol>\n
$\"\"$ <\/p>\n
The regression model for N66 showed good correlation between the spectral data and reference values, especially considering the small amount of samples used in the study.\u00a0 The calibration and validation correlation coefficients are high at 0.997 and 0.995.\u00a0 Three principle components were used.\u00a0 The Standard Error of Prediction (SEP) is equal to 2.5.\u00a0 This error will be reduced when more samples are used over the entire range of values.\u00a0 There is also some spread in some of the data points and this effect will be reduced when a hand-held spectrometer that is in contact with the samples during scanning is used.\u00a0 The high correlation coefficients indicate that the model does obtain correlation from the spectral data and that the error will be less when more samples are used for the calibration.\u00a0 The graph below shows the loading weights plot for the N66 model.<\/p>\n
$\"\"$ <\/p>\n
Peaks (positive or negative) in a loading weights plot for a PLS 1 regression model show the wavelengths that contain relevant information for the model.\u00a0 The plot for the N66 shows peaks in the C-H absorbing areas around 1700nm and 2250nm and the C=O area around 2050nm.\u00a0 These are also the wavelength ranges where visible spectral differences are seen in the first derivative spectra and ranges where one would expect to see spectral changes due to changes in N66.<\/p>\n
$\"\"$ <\/p>\n
The regression model for PP & SB showed good correlation between the spectral data and reference values, especially considering the small amount of samples used in the study.\u00a0 The calibration and validation correlation coefficients are high at 0.998 and 0.997.\u00a0 Two principle components were used.\u00a0 The Standard Error of Prediction (SEP) is equal to 0.8.\u00a0 This error will be reduced when more samples are used over the entire range of values.\u00a0 There is also some spread in some of the data points and this effect will be reduced when a hand-held spectrometer that is in contact with the samples during scanning is used.\u00a0 The high correlation coefficients indicate that the model does obtain correlation from the spectral data and that the error will be less when more samples are used for the calibration.\u00a0 The graph below shows the loading weights plot for the PP & SB model.<\/p>\n
$\"\"$ <\/p>\n
Peaks (positive or negative) in a loading weights plot for a PLS 1 regression model show the wavelengths that contain relevant information for the model.\u00a0 The plot for the PP & SB model shows peaks in the C-H absorbing areas around 1700nm and 2250nm.\u00a0 These are also wavelength ranges where visible spectral differences are seen in the first derivative spectra and ranges where one would expect to see spectral changes due to changes in PP.\u00a0\u00a0 There is no peak at 2050nm and this is further proof that the model is fitting relevant information because PP contains no C=O bonds.<\/p>\n
$\"\"$ <\/p>\n
The regression model for CC & AlTH showed good correlation between the spectral data and reference values, especially considering the small amount of samples used in the study.\u00a0 The calibration and validation correlation coefficients are high at 0.992 and 0.990.\u00a0 One principle component was used.\u00a0 The Standard Error of Prediction (SEP) is equal to 3.4.\u00a0 This error will be reduced when more samples are used over the entire range of values.\u00a0 There was an especially large difference between the high sample value (71.5%) and the low sample values.\u00a0\u00a0 There is also some spread in some of the data points and this effect will be reduced when a hand-held spectrometer that is in contact with the samples during scanning is used.\u00a0 The high correlation coefficients indicate that the model does obtain correlation from the spectral data and that the error will be less when more samples are used for the calibration.\u00a0 The graph below shows the loading weights plot for the CC & AlTH model.<\/p>\n
$\"\"$ <\/p>\n
Peaks (positive or negative) in a loading weights plot for a PLS 1 regression model show the wavelengths that contain relevant information for the model.\u00a0 The plot for the CC and AlTH model shows peaks in the C-H absorbing areas around 1700nm and 2250nm and the C=O bond absorbing area around 2050nm.\u00a0 Calcium carbonate does contain carbon-oxygen bonds but no C-H stretches.\u00a0 However, the fact that the other two components in the samples do contain C-H stretches means that an indirect correlation can be obtained in these wavelength ranges for CC & AlTH. The wavelength ranges where visible spectral differences are seen in the first derivative spectra and ranges where one would expect to see spectral changes due to changes in CC & AlTH.<\/p>\n
<\/p>\n
Discussion and Conclusions<\/u><\/strong><\/p>\n
The results of this study proved the feasibility of measuring Nylon 6,6, Polypropylene & Styrene Butadiene, and Calcium Carbonate & Aluminum Tri-Hydrate from spectral data collected using a Brimrose AOTF-NIR Free Space spectrometer and calibration models.\u00a0 The correlation coefficients for the models were high and there are many ways that the results obtained in this study can be improved upon.\u00a0 When the data from Sample 5 was taken out of the models, there were only four samples used in the study and these samples had many differences in fiber size and texture.\u00a0 The results will improve when more samples over the full range of reference values for all three parameters are added to the calibration models.\u00a0 The results obtained here should also improve when a hand-held spectrometer that comes into contact with the samples is used.\u00a0 There were some variations in the distance of the samples to the optical head of the Free Space spectrometer and this may have contributed to some of the error.\u00a0 Past experience has shown that using one hundred or more samples for a calibration model will increase model robustness and reduce prediction error.\u00a0 Past experience has also shown that the results of a study conducted in the laboratory can be duplicated in a real-life, on-line situation.<\/p>\n
The Brimrose AOTF-NIR spectrometer is the ideal tool for real-time, on-line measurements.\u00a0 Fast, accurate readings can be obtained using no moving parts and without the need to recalibrate the system.\u00a0 In this case, calibration models can be created for each of the parameters measured in this study.\u00a0 Once the models are created, a hand-held spectrometer can be used to scan samples and obtain readings for N66, PP & SB, and CC & AlTH.\u00a0 Overall, this study proved the feasibility of measuring N66, PP & SB, and CC & AlTH from spectral data collected using a Brimrose AOTF-NIR spectrometer.\u00a0 It is recommended that a discussion be held to determine the best method for implementing a Brimrose AOTF-NIR Luminar<\/a> 4030 hand-held spectrometer for real-time measurement of these parameters.<\/p>\n
AOTF<\/a>\n Brimrose<\/a>\n KOH\u5728\u7ebf\u68c0\u6d4b<\/a>\n PET\u7f9f\u503c<\/a>\n PET\u9178\u503c<\/a>\n \u4e01\u82ef<\/a>\n \u4e09\u6c34\u5408\u7269<\/a>\n \u4fbf\u643a\u5f0f\u5149\u8c31\u4eea<\/a>\n \u4fbf\u643a\u8fd1\u7ea2\u5916<\/a>\n \u5085\u91cc\u53f6\u7ea2\u5916\u5149\u8c31\u4eea<\/a>\n \u5206\u5b50\u91cf\u5728\u7ebf<\/a>\n \u542b\u6c34\u7387\u5728\u7ebf\u68c0\u6d4b<\/a>\n \u5728\u7ebf\u7f9f\u503c<\/a>\n \u5728\u7ebf\u8fd1\u7ea2\u5916<\/a>\n \u5728\u7ebf\u8fd1\u7ea2\u5916\u5149\u8c31\u4eea<\/a>\n \u5728\u7ebf\u8fd1\u7ea2\u5916\u5206\u6790\u7cfb\u7edf<\/a>\n \u5728\u7ebf\u9178\u503c<\/a>\n \u5c3c\u9f9966<\/a>\n \u624b\u6301\u5f0f\u5149\u8c31\u4eea<\/a>\n \u6d4b\u5b9a\u7ea4\u7ef4<\/a>\n \u78b3\u9178\u9499<\/a>\n \u7ea2\u5916\u5149\u8c31<\/a>\n \u7ea2\u5916\u5149\u8c31\u4eea<\/a>\n \u7ea2\u5916\u5149\u8c31\u5206\u6790\u4eea\u5668<\/a>\n \u7ea2\u5916\u5728\u7ebf<\/a>\n \u7ea4\u7ef4<\/a>\n \u805a\u4e19\u70ef<\/a>\n \u8fd1\u7ea2\u5916\u5149\u8c31\u5206\u6790\u4eea<\/a>\n \u8fd1\u7ea2\u5916\u5149\u8c31\u6280\u672f<\/a>\n \u8fd1\u7ea2\u5916\u5728\u7ebf\u5149\u8c31\u4eea<\/a>\n \u8fd1\u7ea2\u5916\u5728\u7ebf\u6c34\u5206\u6d4b\u5b9a\u4eea<\/a><\/div>","protected":false},"excerpt":{"rendered":"
\u6d4b\u5b9a\u603b\u7ed3 Brimrose ATOF-NIR\u81ea\u7531\u7a7a\u95f4\u5149\u8c31\u4eea\u7528\u4e8e\u626b\u63cf\u542b\u6709\u4e0d\u540c\u91cf\u5c3c\u9f9966\uff0c\u805a\u4e19\u70ef\u548c\u4e01\u82ef\uff0c\u78b3\u9178\u9499\u548c\u4e09\u6c34\u5408\u7269\u7684\u7ea4\u7ef4\u6837\u54c1\uff0c\u63d0\u4f9b\u4e865\u4e2a\u6837\u672c\uff0c\u6bcf\u4e2a\u6837\u672c\u626b\u63cf5\u6b21\u3002\u6837\u54c1\u7684\u5927\u5c0f\u548c\u8d28\u5730\u5404 <\/p>\n","protected":false},"author":19,"featured_media":154,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[15,16],"tags":[33,34,43,48,47,149,151,53,61,55,49,40,42,36,35,63,41,147,59,138,150,50,51,57,60,146,148,56,58,37,39],"_links":{"self":[{"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/posts\/261"}],"collection":[{"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/comments?post=261"}],"version-history":[{"count":0,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/posts\/261\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/media\/154"}],"wp:attachment":[{"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/media?parent=261"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/categories?post=261"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/tags?post=261"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}