Compositional and Electronic Discrimination Analyses of Taste and Aroma Profiles of Non-Centrifugal Cane Brown Sugars

Volatile aroma components and MS-based electronic nose profiles of dogfruit ( Pithecellobium jiringa ) and stink bean ( Parkia speciosa )

“…These MS-based nose results detailed the volatile-profile differentiations and provided an important chemical markers in a form of discriminative MS dataset as “digital fingerprints” for dogfruit and stink bean during maturity for further development of rapid measurement technology on volatile alterations evaluation of these legumes or their derivative products [21] . The MS-based electronic nose method and chemometric data analysis might thus be applied for monitoring the flavor quality of smelly plant materials in a faster and thorough manner than compositional GC measurement, which confirms the advantageous use of MS-based e-nose profiling technique on differentiation of food flavor [19] , [20] , [21] , [31] , [32] .

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“…3 a versus Fig. 4 a) [19] , [21] . The potential association had been found between discriminant ion masses with MS fragmentation.…”

“…The MS-nose profiles of dogfruit and stink bean were acquired by using a GERSTEL Chemsensor (GERSTEL, Mülheim, Germany) in an Agilent G1888 HSS-7890A GC-5975C MS system (Agilent J&W) [19] . The headspace extraction and MS conditions were set as described above, except for the ion source and interface temperatures, which were both maintained at 250 °C.…”

“…Numerous innovative analytical techniques have been developed to complement the use of conservative methods with common analytical instruments for evaluating food quality traits [12] , [19] , [20] . The improved analytical approaches include reliable techniques for both qualitative and quantitative measurements, and they are most often combined with robust chemometric statistical analysis to discriminate samples.…”

“…The MS-based electronic nose is a non-targeted volatile-profiling technique for differentiating evaluated samples without a chromatography peak separation requirement. This profiling technique works based on the selection of ion masses needed for statistical analysis by pattern-recognition learning methods, and it can display discriminant ion masses of samples’ volatile components as valuable “digital fingerprints” [19] , [21] , [23] .…”