Transferring a large data library of fresh total mixed rations from a benchtop to 2 portable near-infrared spectrometers for on-farm real-time decisions

Pérez‐Marín, Dolores; Roza-Delgado, B. de la; Entrenas, José-Antonio; Garrido-Cuevas, Mar

doi:10.3168/jds.2021-21032

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Beside the marked computation differences, both methods seem fitted to transfer calibration from a monochromator like LAB and a diode array sensor, which would make it easy and effective in using older calibration database for diode array instruments. Applying the same instruments involved in this study (FOSS NIRSystems 6500 & Aurora NIR (GraiNit S.R.L, IT), Pérez-Marín et al 6 successfully transferred the calibration from NIR labbench instrument to portables for a dataset of TMR forage using SW standardization algorithm, which didn’t perform very well in our study with alfalfa samples, although it was satisfatory with grasses. The key point again seems to be the lower spectral correlation between LAB and the portable instruments using alfalfa samples.…”

Section: Resultsmentioning

confidence: 93%

“…These samples were selected because the average and range of chemical composition of this study represented the composition of calibration and validation sets (data not shown). The number of samples was larger than that recommended by Shenk and Westerhaus 24 and Perez Marin et al, 6 which should ensure robust calibration transfer even in the presence of potential spectra variability within samples, that could result from the different sample presentations of the instruments used in this study.…”

Section: Methodsmentioning

confidence: 94%

“…3 Nowadays, NIR spectrometers are commercially available in commercial laboratory settings, but they are also used by nutritionists directly on farms. 4–6…”

Section: Introductionmentioning

confidence: 99%

“…3 Nowadays, NIR spectrometers are commercially available in commercial laboratory settings, but they are also used by nutritionists directly on farms. [4][5][6] NIR spectrometers are available with several design types. 7 While benchtop laboratory NIR instruments can provide more accurate and reliable results, 4 they lack portability and are not feasible for field or on-farm applications.…”

Section: Introductionmentioning

confidence: 99%

“…[15][16][17][18] The majority of studies have focused on calibration transfer between laboratory instruments, but there are limited references available describing performance of calibration transfer from laboratory to handhelds instruments. 6,17 After standardization, calibrations developed on one instrument may perform with lower accuracy when used on another instrument, even of the same type, due to instrumental differences associated with tolerance in optics, detectors and light sources and drifting over time. 19 As Fearn 20 pointed out, there are three methods to transfer the calibrations: first is to make robust calibrations that reduce betweeninstrument variability or adjusting calibrations or adjusting spectra.…”

Section: Introductionmentioning

confidence: 99%

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Forage calibration transfer from laboratory to portable near infrared spectrometers

Yang

Casler

et al. 2023

Journal of Near Infrared Spectroscopy

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Portable near infrared (NIR) spectrometers are now readily available on the market and with their smaller size, weight and cost have provided the opportunity to analyze forages both on farms and directly in the field. As new technologies and new portable NIR instruments become available on the market, calibrations for these instruments become a major constraint due to the costs and time necessary to collect reference data. This study evaluated techniques to transfer calibrations for alfalfa and grass forage samples that were developed for a scanning benchtop monochromator (FOSS 6500, 400–2498 nm, LAB) to a diode array instrument (AuroraNir, 950–1650 nm, DA), a digital light processing instrument (NIR-S-G1, 950–1650 nm, DLP) and a short wavelength instrument (SCiO, 740–1070 nm, SCIO). Alfalfa (N = 612) and grass (N = 516) samples from eight agronomic studies were analyzed by wet chemistry for crude protein, neutral detergent fiber (NDF), acid detergent fiber (ADF), in-vitro digestibility (IVTD) and NDF digestibility (NDFD) and divided into calibration, test-set, standardization and inoculation/prediction datasets. Different calibration transfer strategies were evaluated: Spectral Bias Correction (SBC), Shenk and Westerhaus algorithm (SW), Piecewise Direct Standardization (PDS), Dynamic Orthogonal Projection (DOP) or creating a new calibration using LAB predictions of the inoculation/prediction dataset as reference values. All computations for trimming, calibration, validation and standardization were developed using R. SBC with inoculation was an effective method to transfer calibrations for DA. Validation errors for DA transferred calibrations were about 15% lower than LAB for alfalfa data but 6% greater for grass data. For SCIO after DOP spectral adjustment, predicting errors were slightly greater than LAB for both data sets, while prediction errors with DLP were two to three times greater than LAB even after inoculation. PDS created spectral artifacts in the spectra of all three portables, which then resulted in large validation errors. Using LAB predictions as reference values was suitable only for DA, while DLP and DA had large prediction errors. This study showed that calibration sharing between a benchtop and portable instruments is challenging, but possible depending on the portable technologies and the transfer method. Spectral bias correction plus inoculation was the best method to transfer multivariate models for the forage components’ prediction from LAB to handhelds, particularly for DA. Application of DOP was beneficial for SCIO to successfully maintain performance of the original calibration, while for DLP the prediction models were not accurate. Additional studies are necessary to verify these transferring techniques can also be applied to fresh forages, allowing an easier and extended implementation of NIR analysis directly in fields.

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Section: Resultsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 94%

“…3 Nowadays, NIR spectrometers are commercially available in commercial laboratory settings, but they are also used by nutritionists directly on farms. 4–6…”

Section: Introductionmentioning

confidence: 99%