Random Forest as a Non-parametric Algorithm for Near-infrared (NIR) Spectroscopic Discrimination for Geographical Origin of Agricultural Samples

Lee, Sang-Uk; Choi, Hyeon‐Son; Cha, Kwang-Yul; Kim, Mi-Kyeong; Kim, Jeong-Soo; Youn, Chi Hee; Lee, Su-Heon; Chung, Hoeil

doi:10.5012/bkcs.2012.33.12.4267

Cited by 12 publications

(10 citation statements)

References 29 publications

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“…Trait heritabilities, correlations, and prediction accuracies reported here are therefore valid only for such mixed populations, which would not be typical of breeding populations. The use of ST-RF in this study was valuable in accounting for any potential nonlinear relationship between the variables (Svetnik et al, 2003; Lee et al, 2012; Ghasemi and Tavakoli, 2013). Most importantly, it was relevant in restricting negative prediction of constituents by using average prediction technique obtained from several trees of RF (Breiman et al, 1984; Qi, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the option of nonlinear calibration models has been gaining attention as such models are useful in addressing both linear and nonlinear multivariate relationships. The growing interest in the use of nonlinear models for spectra analyses could be attributed to their comparable accuracy, mathematical simplicity, computational efficiency, and robustness to noise (Breiman, 2001; Lee et al, 2012; Ghasemi and Tavakoli, 2013). Random forests (RF), a nonlinear model, has been effective in multivariate calibrations from modern measuring instruments, including spectrometers, chromatographs, and sensor batteries where it has been used to provide valuable interpretable results.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Correlation, Genome-Wide Association and Genomic Prediction of Portable NIRS Predicted Carotenoids in Cassava Roots

et al. 2019

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Random forests (RF) was used to correlate spectral responses to known wet chemistry carotenoid concentrations including total carotenoid content (TCC), all-trans β-carotene (ATBC), violaxanthin (VIO), lutein (LUT), 15-cis beta-carotene (15CBC), 13-cis beta-carotene (13CBC), alpha-carotene (AC), 9-cis beta-carotene (9CBC), and phytoene (PHY) from laboratory analysis of 173 cassava root samples in Columbia. The cross-validated correlations between the actual and estimated carotenoid values using RF ranged from 0.62 in PHY to 0.97 in ATBC. The developed models were used to evaluate the carotenoids of 594 cassava clones with spectral information collected across three locations in a national breeding program (NRCRI, Umudike), Nigeria. Both populations contained cassava clones characterized as white and yellow. The NRCRI evaluated phenotypes were used to assess the genetic correlations, conduct genome-wide association studies (GWAS), and genomic predictions. Estimates of genetic correlation showed various levels of the relationship among the carotenoids. The associations between TCC and the individual carotenoids were all significant (P < 0.001) with high positive values (r > 0.75, except in LUT and PHY where r < 0.3). The GWAS revealed significant genomic regions on chromosomes 1, 2, 4, 13, 14, and 15 associated with variation in at least one of the carotenoids. One of the identified candidate genes, phytoene synthase (PSY) has been widely reported for variation in TCC in cassava. On average, genomic prediction accuracies from the single-trait genomic best linear unbiased prediction (GBLUP) and RF as well as from a multiple-trait GBLUP model ranged from ∼0.2 in LUT and PHY to 0.52 in TCC. The multiple-trait GBLUP model gave slightly higher accuracies than the single trait GBLUP and RF models. This study is one of the initial attempts in understanding the genetic basis of individual carotenoids and demonstrates the usefulness of NIRS in cassava improvement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic Correlation, Genome-Wide Association and Genomic Prediction of Portable NIRS Predicted Carotenoids in Cassava Roots

et al. 2019

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“…We compared calibration performances from two evaluation protocols: iCheck and Chroma Meter, which have been previously deployed for TCC quantification in cassava roots [13,18]. The benefit of using a non-linear machine learning algorithm over the traditional partial least square (PLS) for carotenoids has been previously reported [16,20,21]; therefore, we compared calibrations from the random forest (RF) and PLS models. The effect of sample preparation on calibration performance has been reported where root mashing to obtain homogeneous samples was beneficial [16].…”

Section: Introductionmentioning

confidence: 99%

Portable Spectroscopy Calibration with Inexpensive and Simple Sampling Reference Alternatives for Dry Matter and Total Carotenoid Contents in Cassava Roots

et al. 2021

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The use of standard laboratory methods for trait evaluation is expensive and challenging, especially for low-resource breeding programs. For carotenoid assessment, rather than the standard HPLC method, these programs mostly rely on proxy approaches for quantitative total carotenoid content (TCC) assessment. To ensure data transferability and consistency, calibration models were developed using TCC iCheck and Chroma Meter proxy methods for the adoption of the alternative near-infrared phenotyping method in cassava. Calibration was developed for dry matter content (DMC) using a simple and inexpensive sampling procedure associated with the proxy TCC protocols. The partial least square (PLS) and random forest (RF) models were compared for the two traits, and the correlation (r) between the actual and predicted values in the training and validation (in bracket) sets of r = 0.85 (0.76) and r = 0.98 (0.82) with PLS and RF, respectively, for iCheck, and r = 0.99 (0.96) and r = 0.99 (0.96) with PLS and RF, respectively, for Chroma Meter, was obtained. The calibration result of r = 0.93 (0.83) and r = 0.99 (0.81) using PLS and RF, respectively, was obtained for DMC. This effort is valuable in carotenoids improvement and supports the ongoing effort in adopting portable spectrometers for rapid and cheap phenotyping in cassava.

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“…Until now, RF have rarely been used for classification of NIRS data, but Lee et al (2012) were able to discriminate agricultural products of different geographical origin with up to 100% accuracy. In contrast, our maximum identification success was 13.3% in T. alpestre , T. caespitum , and T .…”

Section: Discussionmentioning

confidence: 99%

“…Some of the major advantages of RF are the handling of data sets with large variable and small observation numbers and the avoidance of model overfitting ( Breiman, 2001 ). RF has been shown to be very efficient for classification problems, giving more accurate results than other methods ( Svetnik et al, 2003 ; Liu et al, 2013 ) and for tackling biological questions, including via spectral data ( Menze et al, 2009 ; Lee et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species

Kinzner

Wagner

Peskoller

et al. 2015

PeerJ

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Species identification—of importance for most biological disciplines—is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond.

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Random Forest as a Non-parametric Algorithm for Near-infrared (NIR) Spectroscopic Discrimination for Geographical Origin of Agricultural Samples

Cited by 12 publications

References 29 publications

Genetic Correlation, Genome-Wide Association and Genomic Prediction of Portable NIRS Predicted Carotenoids in Cassava Roots

Genetic Correlation, Genome-Wide Association and Genomic Prediction of Portable NIRS Predicted Carotenoids in Cassava Roots

Portable Spectroscopy Calibration with Inexpensive and Simple Sampling Reference Alternatives for Dry Matter and Total Carotenoid Contents in Cassava Roots

A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species

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