•An optimised protein extraction method was developed to extract ATIs from barley.•LC-MRM-MS analysis was used to compare extraction method efficiency.•A sequential two-step protocol consistently increased ATI yield (<12% variation).•The protocol was successfully applied to measure six ATIs across 12 barley cultivars. Plant defense protein α-amylase trypsin inhibitors (ATIs) have been proposed as one of the triggers of non-coeliac gluten sensitivity, however there have been no focused studies on their optimal extraction and quantitation from cereal grains. The efficiency of extraction is of utmost interest for the downstream detection and characterisation. In the present study, three extraction buffers and two modified protocols were investigated using LC-MRM-MS in order to examine their ability to efficiently and repeatably extract ATIs from selected barley cultivars. Initially, three extraction buffers IPA/DTT, urea and Tris-HCl were used to extract ATIs from two selected barley cultivars, Commander and Hindmarsh. The results obtained from the preliminary study showed that IPA/DTT and urea-based buffer extraction could yield ∼70% and ∼45% more ATIs, respectively than a buffer based on Tris-HCl extraction, with all methods showing high repeatability (CV < 15%). A multi-step protocol, employing IPA/DTT and urea improved the extraction efficiency in comparison to the single buffer extraction protocols (p<0.0001). When solutions from parallel extractions using IPA/DTT and urea were combined, the results were comparable (p = 0.99) with a sequential multi-step IPA/DTT-urea protocol. However, the repeatability of the combined process was compromised, as discerned by greater variation (CV>30%). The optimised sequential two-step extraction protocol was successfully used to extract and quantify ATIs from 12 barley cultivars. LC–MS analysis revealed that cv Yagan and cv Scope contain the higher levels (∼143% relative to the average barley ATI content), whereas cultivars Fleet (61%), Baudin (77%) and Commander (79%) contained the lowest levels. The libraries of ATIs identified and the quantitative methods described here provide a foundation for the future application of MS-based quantitative methodologies to detect and quantify ATIs in barley varieties and in food products.