Prediction Accuracy Improvement of Indonesian Dairy Cattle Fiber Feed Compositions Using Near-Infrared Reflectance Spectroscopy Local Database

Abstract

The accuracy of near infrared reflectance spectroscopy (NIRS) depends on the database generated from the conventional wet chemistry (CWC). Currently, the local database of fiber-source feeds for tropical dairy cattle are still limited. The study aimed to compare CWC and NIRS initial database (NIRSID) results, to predict CWC from NIRSID, and to improve the accuracy of NIRS prediction using local database (NIRSLD). Five feeds as sources of fiber (Napier grass, natural grass, corn leaves, corn husk, and rice straw) from 4 areas of dairy cattle farming were used (4 farms from each area). For external calibration, 20 independent Napier grass samples were tested. Samples were analyzed using NIRS and CWC to measure dry matter (DM), ash, crude protein (CP), ether extract (EE), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and silica (Si) to calculate hemicellulose, cellulose, and lignin contents. The results obtained by NIRSID were compared to those obtained by CWC using T-test. Predictions of CWC from the results obtained by NIRSID were attempted using regressions. The NIRSLD was developed by inputting the CWC value to NIRS spectrums. Internal calibration and validation as well as external calibration, were run. The results showed that NIRSID has low capacity in determining CWC (R2<0.683). Calibration using local database (NIRSLD) improved CWC prediction accuracy (residual predictive deviation (RPD) > 2 except for DM, EE, CF, ADL, and lignin). External validation showed that CWC and NIRSLD were similar in all parameters (p<0.05). The ratios of the standard error of prediction (SEP) to the standard error of laboratory (SEL) were > 2 for CP, CF, and ADF. It is concluded that the local database of NIRS of fibersource feeds is necessary to improve the prediction accuracy of local dairy fiber-source feeds values using NIRS.

Estimation of Milk Fatty Acids Health Index as Milk Value Added Determinant using FT-NIRS

“…The validation failed to improve the prediction accuracy. R 2 and RPD found in this study were lower than the result found by Despal et al (2020) for forage quality estimation or Coppa et al (2014) in fresh and thawed milk.…”

“…The higher PUFA/SFA value is found in smallholder dairy farm milk due to the high proportion used of forage (50%) in the ration (Lestari et al, 2015), which contain high PUFA (Collomb et al, 2001). The average LCFA found in the milk from traditional farms was 82.25% which shows a great body storage mobilization (Collomb et al, 2001) due to low nutrient content in the feed, primarily forage (Despal et al, 2020). The average CLA content in the milk sample was 1.360%, higher than the average content of CLA in cow's milk (0.34 -1.07%) (Fritsche et al, 2000).…”

“…A rapid and accurate technique is needed to measure milk fatty acids. Near-Infrared Spectroscopy (NIRS) technology have been developed that allows a multi-parametric analysis of organic compound in less than one minute without sample preparation and solvent requirement (Despal et al, 2020).…”

“…The Accuracy of NIRS is dependent on the database used. Although, milk fatty acids detection using NIR spectroscopy have been shown successfully by González-Martín et al (2020), Núñez-Sánchez (2016) and Bergamaschi (2020), but development local database, improved NIRS prediction accuracy (Despal et al, 2020). So far, there is no tropical milk fatty acid database available.…”

Pola penyediaan dan rantai pasok pakan serat pada musim kemarau di peternakan rakyat sapi perah, Lembang, Kabupaten Bandung Barat

“…Pengujian sampel menggunakan NIRS (near infrared reflectance spectroscopy) sehingga didapatkan data kandungan bahan kering (BK), abu, protein kasar (PK), serat kasar (SK), serat diterjen netral (NDF), dan serat diterjen asam (ADF) pada pakan serat [15].…”