{"id":330,"date":"2019-08-27T11:58:27","date_gmt":"2019-08-27T03:58:27","guid":{"rendered":"https:\/\/www.bihec.com\/brimrose\/?p=330"},"modified":"2019-08-27T11:58:27","modified_gmt":"2019-08-27T03:58:27","slug":"brimrose-aotf-nir%e5%85%89%e8%b0%b1%e6%b3%95%e6%b5%8b%e5%ae%9a%e5%90%84%e7%a7%8d%e6%b7%b7%e5%90%88%e7%89%a9%e4%b8%adpmc%ef%bc%8cpva%e5%92%8c%e9%85%aa%e9%86%87%e7%9a%84%e5%90%ab%e9%87%8f","status":"publish","type":"post","link":"https:\/\/www.bihec.com\/brimrose\/brimrose-aotf-nir%e5%85%89%e8%b0%b1%e6%b3%95%e6%b5%8b%e5%ae%9a%e5%90%84%e7%a7%8d%e6%b7%b7%e5%90%88%e7%89%a9%e4%b8%adpmc%ef%bc%8cpva%e5%92%8c%e9%85%aa%e9%86%87%e7%9a%84%e5%90%ab%e9%87%8f\/","title":{"rendered":"Brimrose AOTF-NIR\u5149\u8c31\u6cd5\u6d4b\u5b9a\u5404\u79cd\u6df7\u5408\u7269\u4e2d%PMC\uff0cPVA\u548c\u916a\u9187\u7684\u542b\u91cf"},"content":{"rendered":"

Brimrose<\/a> AOTF-NIR<\/a><\/u><\/strong>\u5149\u8c31\u6cd5\u6d4b\u5b9a\u5404\u79cd\u6df7\u5408\u7269\u4e2d<\/u><\/strong>%PMC<\/u><\/strong>\uff0c<\/u><\/strong>PVA<\/u><\/strong>\u548c\u916a\u9187\u7684\u542b\u91cf<\/u><\/strong><\/p>\n

I.\u00a0 \u64cd\u4f5c\u603b\u7ed3<\/u><\/strong><\/p>\n

\u672c\u7814\u7a76\u8868\u660e\uff0c\u4f7f\u7528Brimrose AOTF-NIR\u5149\u8c31\u4eea<\/a>\u786e\u5b9a\u8fd9\u4e9b\u5316\u5408\u7269\u7684\u5404\u79cd\u6df7\u5408\u7269\u4e2d\u7684PVA\uff0cPMC\u548cCasucol\u7684\u767e\u5206\u6bd4\u542b\u91cf\u662f\u53ef\u884c\u7684\u3002\u4f7f\u7528\u901a\u8fc7\u5149\u7ea4\u8fde\u63a5\u5230Brimrose\u5149\u8c31\u4eea<\/a>\u7684\u5168\u675f\u63a2\u5934\u6536\u96c6\u6570\u636e\u3002\u5149\u8c31\u4eea\u5c06\u6536\u96c6\u5b9e\u65f6\u5728\u7ebf\u6570\u636e\uff0c\u6bcf1-2\u79d21\u6b21\u3002\u8fdb\u4e00\u6b65\u7684\u7814\u7a76\u548c\u6821\u51c6\u5c06\u4f7fBrimrose\u5149\u8c31\u4eea\u80fd\u591f\u7528\u4e8e\u5b9e\u65f6\u5728\u7ebf\u6df7\u5408\u63a7\u5236\u548c\u5de5\u827a\u751f\u4ea7\u7ebf\u4e2d\u7684\u5b9e\u65f6\u4ea7\u54c1\u63a7\u5236\u3002Brimrose\u7684\u5149\u8c31\u4eea\u975e\u5e38\u575a\u56fa\uff0c\u6ca1\u6709\u8fd0\u52a8\u90e8\u4ef6\uff0c\u5bf9\u673a\u68b0\u632f\u52a8\u548c\u73af\u5883\u53d8\u5316\u4e0d\u654f\u611f\uff0c\u4f7f\u5176\u6210\u4e3a\u5de5\u4e1a\u73af\u5883\u4e2d\u5728\u7ebf\u8fc7\u7a0b\u63a7\u5236\u7684\u7406\u60f3\u4eea\u5668\u8bbe\u5907\u3002<\/p>\n

II.\u7b80\u4ecb<\/u><\/strong><\/p>\n

\u58f0\u5149\u53ef\u8c03<\/a>\u6ee4\u6ce2\u5668\uff08AOTF<\/a>\uff09\u7684\u539f\u7406\u57fa\u4e8e\u5149\u5728\u5404\u5411\u5f02\u6027\u4ecb\u8d28\u4e2d\u7684\u58f0\u6298\u5c04\u3002\u88c5\u7f6e\u7531\u7c98\u5728\u53cc\u6298\u5c04\u6676\u4f53\u4e0a\u7684\u538b\u7535\u5bfc\u5c42\u6784\u6210\u3002\u5f53\u5bfc\u5c42\u88ab\u5e94\u7528<\/a>\u7684\u5c04\u9891\uff08RF\uff09\u4fe1\u53f7\u6fc0\u53d1\u65f6\uff0c\u5728\u6676\u4f53\u5185\u4ea7\u751f\u58f0\u6ce2\u3002\u4f20\u5bfc\u4e2d\u7684\u58f0\u6ce2\u4ea7\u751f\u6298\u5c04\u7387\u7684\u5468\u671f\u6027\u8c03\u5236\u3002\u8fd9\u63d0\u4f9b\u4e86\u4e00\u4e2a\u79fb\u52a8\u7684\u76f8\u6805\uff0c\u5728\u7279\u5b9a\u6761\u4ef6\u4e0b\u6298\u5c04\u5165\u5c04\u5149\u675f\u7684\u90e8\u5206\u3002\u5bf9\u4e8e\u4e00\u4e2a\u56fa\u5b9a\u7684\u58f0\u9891\uff0c\u5149\u9891\u7684\u4e00\u4e2a\u7a84\u5e26\u6ee1\u8db3\u76f8\u5339\u914d\u6761\u4ef6\uff0c\u88ab\u7d2f\u52a0\u6298\u5c04\u3002RF\u9891\u7387\u6539\u53d8\uff0c\u5149\u7684\u5e26\u901a\u4e2d\u5fc3\u76f8\u5e94\u6539\u53d8\u4ee5\u7ef4\u6301\u76f8\u5339\u914d\u6761\u4ef6\u3002<\/p>\n

\"\"<\/p>\n

\u5149\u8c31\u7684\u8fd1\u7ea2\u5916<\/a>\u8303\u56f4\u4ece800nm\u52302500 nm\u5ef6\u4f38\u3002\u5728\u8fd9\u4e2a\u533a\u57df\u6700\u7a81\u51fa\u7684\u5438\u6536\u8c31\u5e26\u5f52\u56e0\u4e8e\u4e2d\u7ea2\u5916\u533a\u57df\u7684\u57fa\u9891\u632f\u52a8\u7684\u6cdb\u9891\u548c\u5408\u9891\u3002\u662f\u57fa\u6001\u5230\u7b2c\u4e8c\u6fc0\u53d1\u6001\u6216\u7b2c\u4e09\u6fc0\u53d1\u6001\u7684\u80fd\u7ea7\u8dc3\u8fc1\u3002\u56e0\u4e3a\u8f83\u9ad8\u80fd\u7ea7\u8dc3\u8fc1\u8fde\u7eed\u4ea7\u751f\u7684\u6982\u7387\u8f83\u5c0f\uff0c\u6bcf\u4e2a\u6cdb\u9891\u7684\u5f3a\u5ea6\u8fde\u7eed\u51cf\u5f31\u3002\u7531\u4e8e\u8dc3\u8fc1\u7684\u7b2c\u4e8c\u6216\u7b2c\u4e09\u6fc0\u53d1\u6001\u6240\u9700\u7684\u80fd\u91cf\u8fd1\u4f3c\u4e8e\u7b2c\u4e00\u7ea7\u8dc3\u8fc1\u6240\u9700\u80fd\u91cf\u7684\u4e8c\u500d\u6216\u4e09\u500d\uff0c\u5438\u6536\u8c31\u5e26\u4ea7\u751f\u5728\u57fa\u9891\u6ce2\u957f\u7684\u4e00\u534a\u548c\u4e09\u5206\u4e4b\u4e00\u5904\u3002\u89e6\u7b80\u5355\u7684\u6cdb\u9891\u4ee5\u5916\uff0c\u4e5f\u4ea7\u751f\u5408\u9891\u3002\u8fd9\u4e9b\u901a\u5e38\u5305\u62ec\u5ef6\u4f38\u52a0\u4e0a\u4e00\u4e2a\u6216\u591a\u4e2a\u632f\u52a8\u65b9\u5f0f\u7684\u4f38\u7f29\u3002\u5927\u91cf\u4e0d\u540c\u5408\u9891\u662f\u53ef\u80fd\u7684\uff0c\u56e0\u800c\u8fd1\u7ea2\u5916\u533a\u57df\u590d\u6742\uff0c\u6709\u8bb8\u591a\u8c31\u5e26\u5f7c\u6b64\u90e8\u5206\u53e0\u52a0\u3002<\/p>\n

\u73b0\u5728\uff0cNIR<\/a>S\u88ab\u7528\u4f5c\u5b9a\u91cf\u5de5\u5177\uff0c\u5b83\u4f9d\u8d56\u5316\u5b66\u8ba1\u91cf\u5b66\u6765\u53d1\u5c55\u6821\u6b63\u7ec4\u6210\u7684\u53c2\u7167\u5206\u6790\u548c\u8fd1\u7ea2\u5916\u5149\u8c31<\/a>\u7684\u5206\u6790\u7684\u5173\u8054\u3002\u8fd1\u7ea2\u5916\u6570\u636e\u7684\u6570\u5b66\u5904\u7406\u5305\u62ec\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6cd5\uff08MLR\uff09\u3001\u4e3b\u6210\u5206\u5206\u6790\u6cd5<\/a>\uff08PCA\uff09\u3001\u4e3b\u6210\u5206\u56de\u5f52\u6cd5\uff08PCR\uff09\u3001\u504f\u6700\u5c0f\u4e8c\u4e58\u6cd5<\/a>\uff08PLS\uff09\u548c\u8bc6\u522b\u5206\u6790\u3002\u6240\u6709\u8fd9\u4e9b\u7b97\u6cd5\u53ef\u4ee5\u5355\u72ec\u6216\u8054\u5408\u4f7f\u7528\u6765\u5f97\u5230\u6709\u4ef7\u503c\u7ec4\u6210\u7684\u5b9a\u6027\u63cf\u8ff0\u548c\u5b9a\u91cf\u9884\u6d4b\u3002<\/p>\n

<\/u>III. \u65b9\u6cd5<\/u><\/strong><\/p>\n

    \n
  1. \u6570\u636e\u6536\u96c6<\/strong><\/li>\n<\/ol>\n

    The samples consisted of a white powder contained in small vials and consisted of varying<\/p>\n

    percentages of the following constituents:<\/p>\n

      \n
    1. PMC-50U (Cellulose ether)<\/li>\n
    2. PVA (polyvinylalcohol)<\/li>\n
    3. Casucol (starch ether)<\/li>\n<\/ol>\n

      There were 10 total pre-prepared samples.\u00a0\u00a0 7 samples had actual values were and the remaining 3 samples were classified as unknowns.\u00a0 There were also 100 grams each of the pure form of the three constituents.\u00a0 Additional samples were prepared using the pure form of each constituent.\u00a0 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.<\/p>\n

      All samples were scanned using the AOTF-NIR spectrometer with a full bundle transflectance probe.\u00a0 The probe was placed in each sample such that its weight was the only force to compact the sample.\u00a0 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.\u00a0 The spectral range was 1200nm to 2300nm with each spectrum consisting of an average of 200 scans.\u00a0 The data were collected directly in the absorption mode.\u00a0 The spectral data were entered into The Unscrambler\u00e4 and PLS 1 regression analysis was performed on each of the constituents.<\/p>\n

      IV.\u00a0\u00a0 Results<\/u><\/p>\n

        \n
      1. Sample weights<\/strong><\/li>\n<\/ol>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
        Samples Prepared by Brimrose<\/td>\n<\/tr>\n
        SAMPLE<\/td>\nWt. PMC<\/td>\nWt. PVA<\/td>\nWt. Casucol<\/td>\n% PMC<\/td>\n% PVA<\/td>\n% Casucol<\/td>\n<\/tr>\n
        1<\/td>\n4.9062<\/td>\n0.0580<\/td>\n0.0272<\/td>\n98.29<\/td>\n1.16<\/td>\n0.54<\/td>\n<\/tr>\n
        2<\/td>\n4.8098<\/td>\n0.0806<\/td>\n0.1050<\/td>\n96.28<\/td>\n1.16<\/td>\n2.10<\/td>\n<\/tr>\n
        3<\/td>\n4.7725<\/td>\n0.0278<\/td>\n0.2000<\/td>\n95.44<\/td>\n0.56<\/td>\n4.00<\/td>\n<\/tr>\n
        4<\/td>\n4.7344<\/td>\n0.1061<\/td>\n0.1744<\/td>\n94.41<\/td>\n2.12<\/td>\n3.48<\/td>\n<\/tr>\n
        5<\/td>\n4.6193<\/td>\n0.1520<\/td>\n0.2250<\/td>\n92.45<\/td>\n3.04<\/td>\n4.50<\/td>\n<\/tr>\n
        6<\/td>\n4.5562<\/td>\n0.2246<\/td>\n0.2501<\/td>\n90.56<\/td>\n4.46<\/td>\n4.97<\/td>\n<\/tr>\n
        7<\/td>\n4.4148<\/td>\n0.1256<\/td>\n0.4489<\/td>\n88.49<\/td>\n2.52<\/td>\n9.00<\/td>\n<\/tr>\n
        8<\/td>\n4.3308<\/td>\n0.3498<\/td>\n0.3004<\/td>\n86.95<\/td>\n7.02<\/td>\n6.03<\/td>\n<\/tr>\n
        9<\/td>\n4.2640<\/td>\n0.3995<\/td>\n0.3505<\/td>\n85.04<\/td>\n7.97<\/td>\n6.99<\/td>\n<\/tr>\n
        10<\/td>\n4.1985<\/td>\n0.4241<\/td>\n0.3741<\/td>\n84.03<\/td>\n8.49<\/td>\n7.49<\/td>\n<\/tr>\n
        11<\/td>\n4.1182<\/td>\n0.4755<\/td>\n0.3755<\/td>\n82.87<\/td>\n9.57<\/td>\n7.56<\/td>\n<\/tr>\n
        12<\/td>\n4.1035<\/td>\n0.4993<\/td>\n0.4005<\/td>\n82.02<\/td>\n9.98<\/td>\n8.00<\/td>\n<\/tr>\n
        13<\/td>\n4.0544<\/td>\n0.2005<\/td>\n0.7496<\/td>\n81.02<\/td>\n4.01<\/td>\n14.98<\/td>\n<\/tr>\n
        14<\/td>\n3.9940<\/td>\n0.2997<\/td>\n0.6999<\/td>\n79.98<\/td>\n6.00<\/td>\n14.02<\/td>\n<\/tr>\n
        15<\/td>\n3.9778<\/td>\n0.0249<\/td>\n1.0014<\/td>\n79.49<\/td>\n0.50<\/td>\n20.01<\/td>\n<\/tr>\n
        16<\/td>\n4.0343<\/td>\n0.625<\/td>\n0.9020<\/td>\n80.71<\/td>\n1.25<\/td>\n18.04<\/td>\n<\/tr>\n
        17<\/td>\n4.0537<\/td>\n0.1000<\/td>\n0.8503<\/td>\n81.01<\/td>\n2.00<\/td>\n16.99<\/td>\n<\/tr>\n
        18<\/td>\n4.0637<\/td>\n0.1374<\/td>\n0.8000<\/td>\n81.26<\/td>\n2.75<\/td>\n16.00<\/td>\n<\/tr>\n
        19<\/td>\n4.1260<\/td>\n0.2870<\/td>\n0.6000<\/td>\n82.31<\/td>\n5.73<\/td>\n11.97<\/td>\n<\/tr>\n
        20<\/td>\n4.6032<\/td>\n0.2497<\/td>\n0.1506<\/td>\n92.00<\/td>\n4.99<\/td>\n3.01<\/td>\n<\/tr>\n
        Pre-prepared samples<\/td>\n<\/tr>\n
        B-1<\/td>\n <\/td>\n <\/td>\n <\/td>\n79.45<\/td>\n0.5<\/td>\n20.0<\/td>\n<\/tr>\n
        B-2<\/td>\n <\/td>\n <\/td>\n <\/td>\n84.0<\/td>\n1.0<\/td>\n15.0<\/td>\n<\/tr>\n
        B-3<\/td>\n <\/td>\n <\/td>\n <\/td>\n88.0<\/td>\n2.0<\/td>\n10.0<\/td>\n<\/tr>\n
        B-4<\/td>\n <\/td>\n <\/td>\n <\/td>\n91.0<\/td>\n4.0<\/td>\n5.0<\/td>\n<\/tr>\n
        B-5<\/td>\n <\/td>\n <\/td>\n <\/td>\n91.0<\/td>\n6.0<\/td>\n3.0<\/td>\n<\/tr>\n
        B-6<\/td>\n <\/td>\n <\/td>\n <\/td>\n91.0<\/td>\n8.0<\/td>\n1.0<\/td>\n<\/tr>\n
        B-7<\/td>\n <\/td>\n <\/td>\n <\/td>\n89.5<\/td>\n10.0<\/td>\n0.5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

         <\/p>\n

        Table 1.<\/strong>\u00a0 Weights and percent values of all samples.<\/p>\n

        2.\u00a0\u00a0\u00a0\u00a0\u00a0 Spectra<\/p>\n

        \"\"<\/p>\n

        Figure 2.\u00a0\u00a0 Absorbance spectra of pre-prepared samples and samples prepared by Brimrose.<\/strong><\/em><\/p>\n

        3. Regressions and Modeling<\/strong><\/p>\n

        \"\"<\/p>\n

        Figure 3.<\/strong>\u00a0 PLS 1 regression model for % PMC.<\/p>\n

        \"\"<\/p>\n

        Figure 4.<\/strong>\u00a0 PLS 1 regression model for % PVA<\/p>\n

        \"\"<\/p>\n

        Figure 5.<\/strong>\u00a0 PLS 1 regression model for % Casucol.<\/p>\n

        The results for these regression models were excellent and showed good correlation between the calibration and validation sets.\u00a0\u00a0 The regression for PMC had a SEC of 0.55 and an SEP of 0.93 with two outliers removed.\u00a0 The regression for PVS had a SEC of 0.35 and an SEP of 0.61 with one outlier removed.\u00a0\u00a0 The regression for Casucol had a SEC of 0.66 and an SEP of 0.98 with one outlier removed.<\/p>\n

          \n
        1. Predictions<\/strong><\/li>\n<\/ol>\n

          3 data points were removed from the data set and models were created using the remaining<\/p>\n

          data points.\u00a0 The models were then used to predict the values for the points taken out and these values were compared to the known values.\u00a0\u00a0 The results were excellent for such a small calibration set.\u00a0 Predictions were then done for the 3 unknown samples.<\/p>\n

           <\/p>\n\n\n\n\n\n\n\n\n\n\n\n
           <\/td>\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 CALCULATED PERCENT<\/td>\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 PREDICTED PERCENT<\/td>\n<\/tr>\n
          SAMPLE<\/td>\n% PMC<\/td>\n%PVA<\/td>\n%CAS.<\/td>\n% PMC<\/td>\n%PVA<\/td>\n% CAS.<\/td>\n<\/tr>\n
          6<\/td>\n90.56<\/td>\n4.46<\/td>\n4.97<\/td>\n90.22<\/td>\n <\/td>\n5.07<\/td>\n<\/tr>\n
          7<\/td>\n88.49<\/td>\n2.52<\/td>\n9.00<\/td>\n <\/td>\n2.17<\/td>\n <\/td>\n<\/tr>\n
          9<\/td>\n85.04<\/td>\n7.97<\/td>\n6.99<\/td>\n <\/td>\n8.44<\/td>\n <\/td>\n<\/tr>\n
          11<\/td>\n82.87<\/td>\n9.57<\/td>\n7.56<\/td>\n80.38<\/td>\n <\/td>\n7.79<\/td>\n<\/tr>\n
          20<\/td>\n92.00<\/td>\n4.99<\/td>\n3.01<\/td>\n <\/td>\n <\/td>\n1.17<\/td>\n<\/tr>\n
          B-2<\/td>\n84.00<\/td>\n1.00<\/td>\n15.00<\/td>\n <\/td>\n1.72<\/td>\n <\/td>\n<\/tr>\n
          B-4<\/td>\n91.00<\/td>\n4.00<\/td>\n5.00<\/td>\n91.13<\/td>\n <\/td>\n <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

           <\/p>\n

          Table 2.<\/strong>\u00a0 Prediction of % values using models created with these samples removed.<\/p>\n

           <\/p>\n\n\n\n\n\n\n
          SAMPLE<\/td>\n% PMC<\/td>\n% PVA<\/td>\n% CASUCOL<\/td>\n<\/tr>\n
          T-1<\/td>\n77.62<\/td>\n5.06<\/td>\n17.79<\/td>\n<\/tr>\n
          T-2<\/td>\n85.45<\/td>\n2.36<\/td>\n11.77<\/td>\n<\/tr>\n
          T-3<\/td>\n97.92<\/td>\n0.64<\/td>\n1.44<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

           <\/p>\n

          Table 3.\u00a0 <\/strong>\u00a0Prediction of % values for the 3 unknown samples.<\/p>\n

            \n
          1. Conclusions and Recommendations<\/li>\n<\/ol>\n

            It is concluded that it is feasible to use the Brimrose AOTF-NIR spectrometer to determine percent values for PMC, PVA, and Casucol.\u00a0\u00a0 Both the regression models and values for the predictions show good correlation between the known values and the values determined from the spectral data.\u00a0\u00a0 It is noted that the PVA and Casucol had larger error values than the PMC.\u00a0 This is because the percent values for PVA and Casucol are much smaller than those for PMC.\u00a0 The small number of data points used for this model definitely contributed to the error.\u00a0 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.\u00a0 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.<\/p>\n

             <\/p>\n

            AOTF<\/a>\nBrimrose<\/a>\nKOH\u5728\u7ebf\u68c0\u6d4b<\/a>\nPET\u7f9f\u503c<\/a>\n\u4fbf\u643a\u5f0f\u5149\u8c31\u4eea<\/a>\n\u4fbf\u643a\u5f0f\u8fd1\u7ea2\u5916<\/a>\n\u5085\u91cc\u53f6\u7ea2\u5916\u5149\u8c31\u4eea<\/a>\n\u5206\u5b50\u91cf\u5728\u7ebf<\/a>\n\u5728\u7ebf\u8fd1\u7ea2\u5916\u5149\u8c31\u4eea<\/a>\n\u5728\u7ebf\u9178\u503c<\/a>\n\u624b\u6301\u5f0f\u5149\u8c31\u4eea<\/a>\n\u6df7\u5408\u7269\u4e2d%PMC<\/a>\n\u7ea2\u5916\u5728\u7ebf<\/a>\n\u805a\u916f<\/a>\n\u8fd1\u7ea2\u5916\u5149\u8c31\u5206\u6790\u4eea<\/a>\n\u8fd1\u7ea2\u5916\u5728\u7ebf\u5149\u8c31\u4eea<\/a>\n\u9178\u5ea6<\/a><\/div>","protected":false},"excerpt":{"rendered":"

            Brimrose AOTF-NIR\u5149\u8c31\u6cd5\u6d4b\u5b9a\u5404\u79cd\u6df7\u5408\u7269\u4e2d%PMC\uff0cPVA\u548c\u916a\u9187\u7684\u542b\u91cf I.\u00a0 \u64cd\u4f5c\u603b\u7ed3 \u672c\u7814\u7a76\u8868\u660e\uff0c\u4f7f\u7528Brimrose AOTF-NIR\u5149\u8c31\u4eea\u786e\u5b9a\u8fd9\u4e9b\u5316\u5408\u7269\u7684\u5404\u79cd <\/p>\n","protected":false},"author":19,"featured_media":174,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[33,34,43,48,53,62,55,49,35,41,59,162,60,45,56,37,44],"_links":{"self":[{"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/posts\/330"}],"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=330"}],"version-history":[{"count":0,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/posts\/330\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/media\/174"}],"wp:attachment":[{"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/media?parent=330"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/categories?post=330"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bihec.com\/brimrose\/wp-json\/wp\/v2\/tags?post=330"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}