Estimating Hydrogen Cyanide in Forage Sorghum (Sorghum bicolor) by Near-Infrared Spectroscopy Glen P. Fox,* ,†,‡ Natalie H. O’Donnell, § Peter N. Stewart, ∥,⊥ and Roslyn M. Gleadow § † Centre for Nutrition and Food Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, Queensland 4350, Australia ‡ Queensland Department of Employment and Economic Development and Innovation, Lesley Research Centre, Toowoomba, Queensland 4350, Australia § School of Biological Sciences, Monash University, Clayton, Victoria 3800, Australia ∥ Pacific Seeds, Toowoomba, Queensland 4350, Australia ABSTRACT: Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (NIRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400−2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400−750 nm), shortwave (800−1100 nm) and near-infrared (NIR) (1100−2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R 2 ) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R 2 = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R 2 = 0.847 and standard error of calibration (SEC) = 0.050% and a R 2 = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with CO carbonyl stretch and the latter associated with C−N−C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency. KEYWORDS: Dhurrin, feed quality, hydrogen cyanide, NIRS, wavelengths ■ INTRODUCTION Cyanogenesis is the process whereby plants release hydrogen cyanide (HCN) from in situ cyanide-containing compounds. While cyanogenic glycosides are nontoxic, in the presence of certain enzymes, these compounds are hydrolyzed to produce HCN, which is highly toxic. The development of HCN may play a role in plant defense against herbivores. 1 In sorghum, the cyanogenic glycoside dhurrin is synthesized from the amino acid tyrosine in a series of steps catalyzed by two P450s and a UGT- transferase. Dhurrin is broken down to HCN when it is mixed with specific β-glucosidases (dhurrinase). 2 Dhurrin and dhurrinase are spatially separated in the living plant, such that HCN is only released when the tissue is damaged, consistent with its putative role in herbivore defense. The amount of cyanide able to be released from dhurrin is known as the cyanide potential (HCNp). Near-infrared spectroscopy (NIRS) is an analysis tool used routinely in agricultural sciences. Since its development in the 1950s, it has become the main work-horse for cereal-based plant breeding programs, 3 as well as finding applications in forage testing, 4 remote sensing on plants for growth, 5 and crop nutrition status. 6 Specifically, in cereal plant breeding applications, NIRS has been mainly used in crops, such as wheat or barley, where there is a quality specification on commercially delivered crops. 3 For grain sorghum, no target quality specifications exist, although the opportunity to predict feed traits exists using NIRS. 7 In forage sorghum, NIRS has been used to estimate characteristics, such as chemical composition and feed quality. 8 To date, there has been one published report describing the NIRS estimation of the antinutritional factor, namely, HCNp, in forage sorghum 9 using a partial least-squares method. Recent calibration development strategies use a partial least- squares (PLS) regression approach, i.e., combining all spectral data, of up to 1050 wavelengths. This approach has been used successfully in a number of plant-based agricultural applications, particularly breeding. 3 However, it is possible is to use only a few speci fic wavelengths [multiple linear regression (MLR)] that are correlated to the trait of interest. 10 The early NIRS instruments used a limited number of specific filters (speci fic wavelengths) and for very few grain traits, such as moisture, protein, and lipid. 11 The aim of this study was to determine the suitability of NIRS to estimate HCNp in forage sorghum. A NIRS calibration has been reported, and PLS and MLR models were compared to ascertain if one approach provided a better calibration than Received: August 8, 2011 Revised: May 14, 2012 Accepted: May 17, 2012 Published: May 17, 2012 Article pubs.acs.org/JAFC © 2012 American Chemical Society 6183 dx.doi.org/10.1021/jf205030b | J. Agric. Food Chem. 2012, 60, 6183−6187