Using near infrared reflectance product library files to improve prediction accuracy and reduce calibration costs.

Deriving a near infrared reflectance spectroscopy (NIRS) calibration for new samples is an expensive and time-consuming process. Often a laboratory analyzing samples with NIRS will receive new samples that are spectrally different from the library samples for that product. This study was performed to develop a method to: (i) identify previously analyzed samples in a product library that best match the spectra of the new samples for a local calibration, (ii) compare the prediction accuracy of the broad product library calibration to the local calibration, and (iii) if necessary, expand the local calibrations with a few of the new samples when greater prediction accuracy is required. Three groups of new samples were obtained for the study. They were 68 hay samples, 106 grass samples, and 110 whole-plant corn Zea mays L.) samples. Every third sample was reserved for validation. A product library file of 2176 hay, haylage, and fresh forage spectra supplied the product calibration and similar spectra for the hay and grass samples, and a corn silage product library file of 309 spectra supplied the product calibration and similar spectra for the whole-plant corn samples. The spectra in the library most similar to the new samples were identified and selected with a new program, MATCH. Local calibrations were developed from these selected samples. Calibration accuracy was tested with the new samples reserved for validation using equations developed from the product library. In 7 of the 12 comparisons, the local calibrations were more accurate than the broad-product library calibrations, but only two of these calibrations were acceptable. By expanding the local calibrations with 10 samples of each new group and recalibrating, accuracy of the expanded calibrations was similar to the accuracy of custom calibrations derived from the new samples not reserved for validation. Only 30 samples out of 284 needed new reference values to obtain acceptable prediction accuracy. This would.