Utafiti huu unalenga kutofautisha kisayansi tofauti za aroma na taste kati ya commercial forms mbili za “Boju” chrysanthemum, ambayo hutumiwa kwa jadi nchini China kama herbal tea na medicinal-edible plant source. Makundi mawili ya commercial samples yaliyosomwa yanaitwa Da Boju, yaani DBJ, na Xiao Boju, yaani XBJ. Watafiti hawakulinganisha makundi haya kwa kuangalia au traditional sensory evaluation pekee, bali walitumia electronic nose, electronic tongue, gas chromatography–ion mobility spectrometry, yaani GC-IMS, pamoja na chemometric analyses.

Ujumbe mkuu wa utafiti ni huu: ingawa DBJ na XBJ zinaweza kuonekana kama commercial Boju products zinazofanana, aroma na taste profiles zake zinaweza kutenganishwa kwa vipimo. Katika electronic nose data, hasa W2W sensor ilikuwa muhimu kutofautisha samples mbili. Kwa sababu sensor hii ni sensitive kwa aromatic compounds na organic sulfur compounds, tofauti ya harufu kati ya DBJ na XBJ inaweza kuhusiana na volatile compounds za aina hizi.

Katika electronic tongue analysis, Boju samples zote mbili zilionyesha responses dhahiri katika bitterness, umami, richness na sweetness. Hata hivyo, DBJ ilitofautiana na XBJ kwa stronger umami, more persistent bitter aftertaste na slight saltiness. Katika GC-IMS analysis, jumla ya 125 volatile signals zilitambuliwa na 66 zika-annotate kama compounds. Chemometric analyses zilionyesha kwamba samples mbili zinaweza kutenganishwa wazi kwa volatile-compound profiles. Terpenoids na derivatives zake zilijitokeza kama main chemical drivers za aroma difference.

Kulingana na OPLS-DA model, 28 characteristic volatile markers zina role muhimu katika kutofautisha commercial Boju forms mbili. Miongoni mwao ni alpha-pinene, beta-pinene, alpha-phellandrene, 1,8-cineole, camphor, linalool, gamma-terpinene, alpha-terpineol, longifolene na bornyl acetate. Hata hivyo, structures za baadhi ya markers hazikutambuliwa definitively; hasa signals namba 6, 7, 14, 15, 16 na 17 zimependekezwa kwa additional validation kupitia high-resolution mass spectrometry au two-dimensional GC-MS katika siku zijazo.

Boju ni commercial herbal product inayohusishwa na Bozhou katika Anhui Province, China, na hutumiwa kama chrysanthemum tea. Study inaeleza kwamba Boju ina nafasi muhimu kati ya medicinal na edible chrysanthemums nchini China. Lakini ni muhimu kutambua kwamba research hii si clinical study ya kuthibitisha health effects za Boju. Mada yake ni jinsi commercial forms mbili zinavyoweza kutofautishwa kwa aroma, taste na volatile-compound profiles.

Aroma ni muhimu sana katika quality ya food na herbal products. Kupendwa kwa herbal tea hakutegemei appearance au label tu; smell, taste kwenye palate, bitterness balance, aftertaste na aromatic depth pia huamua quality perception. Katika traditional quality assessment, experts mara nyingi hunusa, kuonja na kuchunguza product. Njia hii ni valuable kwa experienced people, lakini human perception hubadilika kati ya watu. Fatigue, ambient odor, expectation, experience na hata perception ya mtu huyo huyo katika siku tofauti zinaweza kuathiri result.

Kwa hiyo electronic nose na electronic tongue kama bionic sensing systems zinatumika zaidi katika food quality control. Electronic nose inategemea sensor arrays zinazojaribu kuiga human olfaction. Kila sensor hutoa response tofauti kwa chemical group fulani. Sensor moja pekee haitoshi kusema “compound hii ni hii hasa”; lakini response pattern ya sensor array inaweza kutumika kama fingerprint yenye nguvu ya kulinganisha odor profiles za samples.

Electronic tongue pia inajaribu ku-quantify taste perception. Sourness, sweetness, bitterness, umami, saltiness, astringency, bitter aftertaste, astringent aftertaste na richness, yaani umami aftertaste, hupimwa kupitia sensor responses. Systems hizi hazibadilishi human tasting panel kabisa, lakini zinaweza kutoa rapid, reproducible na more objective preliminary evaluation.

Katika utafiti huu, electronic nose na electronic tongue data zimeunganishwa na GC-IMS. GC-IMS ni technique yenye nguvu ya kutenganisha na ku-characterize volatile organic compounds. Gas chromatography hutenganisha compounds kwa retention differences ndani ya column. Ion mobility spectrometry hupima drift times za ionized molecules katika electric field. Hivyo gas-chromatographic retention behavior na ion-mobility behavior hutathminiwa pamoja.

Umuhimu wa GC-IMS katika study hii ni mkubwa. Katika herbal tea kama Boju, aroma hutokana na complex profile ya volatile compounds nyingi zenye low concentrations. Kuangalia compounds chache moja moja mara nyingi hakutoshi. GC-IMS inaweza kuonyesha volatile-compound fingerprint ya sample kama visual maps, hivyo kufanya DBJ na XBJ differences zionekane kwa holistic way.

Tatizo kuu la utafiti ni jinsi commercial forms mbili za Boju, DBJ na XBJ, zinavyoweza kutofautishwa sensory na chemically. Kulingana na article, forms hizi zina morphology na sensory differences ambazo zinaweza kuathiri consumer preference na market value. Hata hivyo, systematic studies zinazoeleza aroma na taste discrimination kati ya forms hizi ni chache. Research inapendekeza multi-analytical platform kujaza gap hii.

Samples zilinunuliwa katika medicinal-material market ya Bozhou, Anhui. Kwa kila commercial group, independent batches tatu zilikusanywa. Samples zilitambuliwa kama dried chrysanthemum flower heads na Associate Professor Yang Qingshan kutoka Anhui University of Chinese Medicine. DBJ samples zilitambuliwa katika muktadha wa Chrysanthemum × morifolium ‘Damaya’ cv. nov., na XBJ kama Chrysanthemum × morifolium ‘Boju’ cv. nov. Identification hii ni muhimu kwa sababu comparison si random tea samples mbili, bali distinct product groups ndani ya commercial Boju.

Electronic nose analysis ilitumia Pen3 Plus system yenye 10 metal oxide sensors. Kila sensor ina sensitivity tofauti kwa chemical groups. Kwa mfano, W1C ni sensitive kwa benzene-like na aromatic compounds, W5S kwa nitrogen oxides, W1W kwa inorganic sulfur compounds, W2S kwa alcohols, ethers, aldehydes na ketones, na W2W kwa aromatic compounds na organic sulfur compounds. Kwa kila sample, 3 g powder iliwekwa katika special measurement container, kufungwa kwa Parafilm na kuachwa overnight katika constant temperature ili volatiles zifikie equilibrium. Kila sample ilipimwa mara tatu.

Electronic nose radar graph ilionyesha clear differences katika sensor responses za DBJ na XBJ. Highest responses zilionekana katika W1W na W2W, zikifuatiwa na W5S. Hii inaashiria kwamba sulfur-containing volatiles, aromatic/terpenoid compounds na nitrogen-oxide-sensitive signal groups zinaweza kuwa muhimu katika Boju aroma profile. DBJ ilikuwa na W1W na W2W responses kubwa zaidi kuliko XBJ, ikionyesha stronger signal katika aromatic na sulfur-containing volatile profile.

Electronic nose data zilitathminiwa pia kwa PCA na OPLS-DA. PCA hupunguza sensor variables nyingi kuwa components chache ili kuonyesha overall sample separation. Katika electronic nose PCA, first principal component ilieleza %56,1 ya total variance na second principal component %25,6. Combined explained variance ilikuwa %81,7. Hii inaonyesha electronic nose data zina-capture DBJ/XBJ separation kwa kiasi kikubwa.

Katika OPLS-DA, DBJ na XBJ zilitenganishwa kuwa groups mbili. VIP value huonyesha variable ipi inachangia zaidi classification. Variables zenye VIP > 1 kwa kawaida huchukuliwa kama important discriminators. W2W sensor ilijitokeza kama high-contribution variable katika electronic nose data. Hii inaunga mkono role ya aromatic compounds na organic sulfur compounds katika odor difference. Pia permutation test yenye 200 repetitions ilifanywa na model ikaripotiwa kutokuwa overfitted.

Electronic tongue analysis ilitumia SA402BPlus-EX system. Taste attributes zilikuwa sourness, sweetness, bitterness, umami, saltiness, astringency, bitter aftertaste, astringent aftertaste na richness, yaani umami aftertaste. Kwa sample preparation, 1,5 g powder iliongezewa 100 mL distilled water, ikafanyiwa 100 W ultrasonic extraction kwa 30 minutes, kisha centrifuge katika 2800 r/min kwa 15 minutes. Filtrate iliingia kwenye electronic tongue measurement. Kila sample iliandaliwa in duplicate, measurement ikafanywa kwa four cycles na data za last three cycles zikatumiwa.

Electronic tongue radar graph ilionyesha bitterness, umami, richness na sweetness responses dhahiri kwa Boju forms zote mbili. Sourness response ilikuwa chini ya -13 taste reference point, hivyo haikuchukuliwa kuwa meaningful reference value. Saltiness ilitoa difference muhimu. XBJ saltiness response ilikuwa -7,48, chini ya -6 taste zero point, hivyo ilitafsiriwa kama no or very weak saltiness. DBJ saltiness response ilikuwa -4,34, juu ya taste zero point, ikionyesha slight saltiness.

Taste profiles za DBJ na XBJ si tofauti kabisa. Sweetness, bitterness, astringency na astringent aftertaste kwa ujumla zinafanana. Lakini DBJ ina higher bitter aftertaste, umami, richness na saltiness. Hii ni muhimu kwa consumer experience kwa sababu quality perception ya herbal tea haitegemei first-sip taste pekee, bali pia aftertaste. Persistent bitter aftertaste na stronger umami character ya DBJ ni miongoni mwa features zinazotofautisha samples.

Katika electronic tongue PCA, first principal component ilikuwa %63,4 na second %19,1, na total explained variance %82,5. Hii inaonyesha good taste separation. OPLS-DA pia ilitenganisha groups. VIP scores zilikuwa sourness 1,84036, saltiness 1,36991, umami 1,20294 na bitter aftertaste 1,00416. Hata hivyo, sourness response ilikuwa chini ya taste reference point na haikutumiwa katika practical interpretation. Kwa hiyo saltiness, umami na bitter aftertaste zilijitokeza kama meaningful taste discriminators.

GC-IMS analysis ndiyo chemical backbone ya study. Kwa kila sample, 2,0 g iliwekwa katika 20 mL headspace vial. 10 µL ya 10 ppm 4-methyl-2-pentanol iliongezwa kama internal standard. Headspace incubation ilifanywa katika 80°C kwa 20 minutes. Injection volume ilikuwa 200 µL na injection ilikuwa splitless. MXT-5 column ilitumika katika GC-IMS system, high-purity nitrogen ilikuwa carrier na drift gas, positive-ion mode ilitumika na drift tube temperature ilikuwa 45°C.

GC-IMS graphs hutoa visual interpretations tatu. Katika 3D spectrum, x-axis ni drift time, y-axis retention time na z-axis signal peak amplitude. Katika 2D map, volatile compounds huonekana kama colored spots. Color intensity huonyesha signal strength; dark blue ni lower na red ni higher signal. Katika difference graph, DBJ ilitumika kama reference dhidi ya XBJ. White areas zinaonyesha similar content, red higher signal katika target sample na blue lower signal.

GC-IMS results zilionyesha clear differences katika volatile-compound composition kati ya DBJ na XBJ. Baada ya relative drift time karibu 1,5 a.u., baadhi ya volatile peaks katika XBJ zilikuwa na higher intensity kuliko DBJ. Visual separation hii iliungwa mkono na fingerprint na chemometric analyses.

Jumla ya 125 signal peaks zilipatikana na 66 compounds zika-annotate. Compounds zilijumuisha aldehydes, alcohols, esters, ketones, furans, terpenoids na related groups. Signals 17 zilipewa numbers na structures zake hazikutambuliwa definitively. Hii ni limitation muhimu. GC-IMS inatoa powerful fingerprint analysis, lakini si kila signal inaweza kupata exact structural identification. Unknown markers zinahitaji future identification kwa advanced techniques.

Fingerprint analysis ilichunguza compounds zenye relative abundance kubwa katika DBJ au XBJ. DBJ ilikuwa na higher 3-(methylthio)propanal, isopulegyl acetate na baadhi ya unidentified compounds. Borneol-M signal pia ilikuwa higher katika DBJ. XBJ ilikuwa na higher alpha-terpinene, gamma-terpinene, d-camphor, alpha-terpineol, linalool na multiple forms za sabinene hydrate.

Matokeo haya ni muhimu kwa aroma chemistry. Terpenoids ni broad family ya compounds zinazoweza kuchangia floral, herbal, resinous, refreshing, spicy au medicinal odors katika herbal teas. Characteristic aroma ya chrysanthemum tea inatarajiwa kuhusishwa na volatiles kama terpenoids na aldehydes. Consistency kati ya GC-IMS na electronic nose results inaonyesha kwamba odor differences zilizogunduliwa na sensors zina chemical basis.

Katika PCA ya GC-IMS data, first principal component ilikuwa %63,6 na second %28,8, na total explained variance %92,4. Hii ni high ratio na inaonyesha strong representation ya DBJ/XBJ separation. OPLS-DA score plot ilitenganisha samples kuwa two distinct groups bila regional overlap. XBJ group ilionyesha some within-group variability, ambayo study ilijadili kuwa inaweza kuhusiana na harvest time na processing techniques.

Katika OPLS-DA, 28 volatile compounds/signals zenye VIP > 1 zilichaguliwa kama markers. Baadhi ya highest VIP scores ni: (R)-alpha-pinene-T 3,49745; Compound 16 3,37239; beta-pinene-P 2,48692; alpha-phellandrene-D 2,47627; (E,E)-2,4-octadienal-D 2,42856; 1,8-cineole-D 2,38623; longifolene 2,19984; d-camphor-D 2,05218; methyl 2-methoxybenzoate 2,00524; (Z)-sabinene hydrate-M 1,80307; linalool-M 1,72702; gamma-terpinene-M 1,59664; alpha-terpineol-D 1,57303; bornyl acetate-M 1,39957; borneol-D 1,34498; benzoic acid 1,26304 na isopulegyl acetate 1,26024.

List hii inaonyesha kwamba single compound haitoshi kwa Boju quality discrimination. Aroma difference inatokana na chemical fingerprint ya multiple volatile compounds pamoja. Baadhi ya compounds zinaweza kuwa higher katika DBJ na nyingine katika XBJ. Kwa hiyo multi-marker panel ina maana zaidi kuliko single marker.

Heat map inaonyesha 28 volatile markers zikiwa na different intensity patterns katika DBJ na XBJ samples. Red tones ni higher relative signals na blue tones lower. Samples zilitengeneza clustering patterns tofauti kwa marker set hii. Hivyo GC-IMS haikutoa compound list pekee, bali visual na statistical classification basis kwa commercial product discrimination.

Connection ya study na maisha ya kawaida ni direct. Consumer anaweza kuhisi aroma na taste differences kati ya commercial forms za plant ileile lakini asiweze kueleza sababu. Research hii inaonyesha kwamba differences kama hizo zinaweza kupimwa objectively kwa electronic sensors na volatile-compound analysis. Kwa quality control, hii ni muhimu kwa product classification, label accuracy, commercial standardization na consumer confidence.

Zamani herbal-product quality iliamuliwa kwa morphology, color, smell, expert assessment na basic chemical-content analyses. Leo food na herbal markets zinahitaji faster, objective na multivariate analytical systems. Electronic nose, electronic tongue na GC-IMS ni modern tools zinazoweza kujibu need hiyo. Katika siku zijazo, approach inaweza kutumika si kwa Boju tu, bali pia saffron, ginger, citrus peel, ginseng, medicinal mushrooms na other high-value herbal products kwa quality control na origin authentication.

Mapungufu ni wazi. Kwanza, study ni preprint bila peer review. Pili, samples ni commercial Boju groups mbili tu na independent batches tatu kwa kila group; wider geographical sampling, different harvest years, processing conditions na storage durations hazijachunguzwa. Tatu, baadhi ya important GC-IMS signals bado hazina exact chemical identification. Nne, ingawa electronic sensor data zinaonyesha strong separation, study haina comprehensive trained human sensory panel au consumer-perception validation.

Utafiti unasema kwamba DBJ na XBJ zinaweza kutenganishwa wazi kwa electronic nose, electronic tongue na GC-IMS; aroma discrimination inaangazia aromatic/sulfur volatiles na hasa terpenoid markers; taste discrimination inaonyesha slight saltiness, stronger umami na persistent bitter aftertaste kwa DBJ; na GC-IMS inatoa strong volatile fingerprint kupitia 125 signals na 66 annotated compounds.

Utafiti hausumi kwamba analyses hizi zinathibitisha medicinal effects za Boju, kwamba DBJ au XBJ ni superior kwa health, au kwamba commercial Boju products zote zinaweza classified definitively kwa samples hizi mbili pekee. Study inatoa analytical method na scientific data kwa aroma, taste na chemical quality discrimination.

Mbinu na Matokeo ya Utafiti

Mbinu ya utafiti inategemea kuchunguza commercial forms mbili za Boju chrysanthemum tea, DBJ na XBJ, katika levels tatu: odor profile kwa electronic nose, taste profile kwa electronic tongue na volatile-compound fingerprint kwa GC-IMS. Data hizi zimetathminiwa kwa chemometric methods kama PCA na OPLS-DA.

Sample material:

Electronic nose method:

Electronic nose findings:

• W1W na W2W sensors zilitoa highest responses.
• W5S sensor pia ilionyesha clear response.
• DBJ ilikuwa na W1W na W2W responses higher kuliko XBJ.
• Hii inaonyesha aromatic compounds na sulfur-containing volatiles zinaweza kuwa important kwa odor discrimination.
• Katika electronic nose PCA, PC1 ilichangia %56,1 na PC2 %25,6.
• Total explained variance ilikuwa %81,7.
• OPLS-DA model ilitenganisha DBJ na XBJ kuwa groups mbili.
• W2W sensor ilijitokeza kama discriminative variable kwa VIP.
• Permutation test yenye 200 repetitions haikuunga mkono model overfitting.

Electronic tongue method:

Electronic tongue findings:

• Samples zote mbili zilikuwa na strong bitterness, umami, richness na sweetness responses.
• Sourness response ilikuwa chini ya -13 reference point, hivyo haikutumika katika practical interpretation.
• XBJ saltiness response ilikuwa -7,48; chini ya -6 taste zero point, hivyo no/very weak saltiness.
• DBJ saltiness response ilikuwa -4,34 na ilionyesha slight saltiness.
• DBJ ilikuwa na higher bitter aftertaste, umami, richness na saltiness kuliko XBJ.
• Katika electronic tongue PCA, PC1 ilichangia %63,4 na PC2 %19,1.
• Total explained variance ilikuwa %82,5.
• OPLS-DA model ilitenganisha DBJ na XBJ kwa taste profile.

Electronic tongue VIP scores:

GC-IMS method:

GC-IMS general results:

Fingerprint-analysis differences:

• DBJ had higher signals for 3-(methylthio)propanal, isopulegyl acetate and some unidentified compounds.
• Borneol-M signal was higher in DBJ than XBJ.
• XBJ had higher signals for alpha-terpinene, gamma-terpinene, d-camphor, alpha-terpineol, linalool and sabinene hydrate forms.
• XBJ also showed higher relative abundance of isobutyl isobutyrate, (E)-2-heptenal, furfural, 2-butylfuran, methyl pentanoate, longifolene and some other compounds.

GC-IMS chemometric findings:

• In GC-IMS PCA, PC1 contributed %63,6 and PC2 %28,8.
• Total explained variance was %92,4.
• DBJ and XBJ were separated clearly into two groups in OPLS-DA score plot.
• Some within-group variability was observed in XBJ; possible roles of harvest time and processing conditions were discussed.
• Using VIP>1 criterion, 28 volatile compounds/signals were selected as discriminative markers.
• Permutation test with 200 repetitions supported that OPLS-DA model was not overfitted.

28 discriminative volatile markers identified by GC-IMS:

Table haionyeshi unidentified signals zote moja moja; study inasisitiza kwamba exact structures za markers namba 6, 7, 14, 15, 16 na 17 zinahitaji kufafanuliwa katika future work.

Meaning of figures and graphs:

• Electronic nose radar graph shows DBJ and XBJ differ especially in W1W and W2W sensor responses, supporting possible roles of sulfur-containing and aromatic volatiles.
• Electronic nose PCA and OPLS-DA graphs show sensor data can separate two commercial Boju forms into distinct clusters.
• Electronic tongue radar graph shows bitterness, umami, richness and sweetness profiles of both samples, and more pronounced saltiness, umami and bitter aftertaste in DBJ.
• Electronic tongue VIP graph shows contributions of saltiness, umami and bitter aftertaste to taste discrimination.
• GC-IMS 3D and 2D spectra show DBJ and XBJ form different intensity regions in volatile-compound maps.
• GC-IMS difference graph uses colors to show signals that are higher or lower in each sample.
• Fingerprint graph compares the complete volatile-signal patterns side by side and shows compounds dominant in DBJ or XBJ.
• GC-IMS PCA and OPLS-DA graphs show strong separation of the two commercial samples based on volatile data.
• 28-marker heat map visually summarizes different abundance patterns of discriminative volatile compounds in DBJ and XBJ.

Overall technical conclusion: When electronic nose, electronic tongue and GC-IMS data are used together, aroma and taste profiles of DBJ and XBJ can be separated reliably. Electronic nose captures general odor-sensor differences, electronic tongue captures taste separation, and GC-IMS reveals the volatile-compound basis of these differences. Chemometric analyses convert this multidimensional data into classifiable quality indicators.

Maelezo ya Chanzo na Mbinu

Makala hii inatokana na utafiti wa Xiao-su Xiang, Yun-yun Sun, Jun-shan Gao, Da-hui Li na Qing-shan Yang wenye kichwa “Flavor profiling and quality discrimination of commercial ‘Boju’ (Chrysanthemum morifolium) using GC-IMS coupled with chemometrics”. Utafiti unalinganisha aroma, taste na volatile-compound profiles za commercial Boju chrysanthemum tea forms mbili, DBJ na XBJ.

Chanzo ni preprint research paper inayopatikana kwenye SSRN. Kwa kuwa manuscript inasema wazi “This preprint research paper has not been peer reviewed”, study haijapitia peer review. Kwa hiyo findings zinapaswa kuchukuliwa kama multi-sensor na GC-IMS-based preprint research data katika commercial Boju samples, si final peer-reviewed publication.

Maudhui haya yanategemea sampling information, electronic nose sensor responses, electronic tongue taste profile, GC-IMS volatile-compound spectra, fingerprint analysis, PCA, OPLS-DA, VIP scores, permutation tests, 28 discriminative markers na discussion results. Hakuna claims zisizokuwepo katika PDF, kama health effects, clinical benefits, therapeutic superiority, consumer preference guarantee au commercial-value guarantee, zilizoongezwa.

Study haikutoa direct mathematical formulas. Chemometric concepts kama PCA, OPLS-DA na VIP zilitumika ku-classify multivariate data na kutambua sensors au volatile compounds muhimu kwa discrimination. Katika makala hii, methods hizi zimeelezwa kupitia study results na numerical contribution ratios zilizoripotiwa.

Mapungufu muhimu ni non-peer-reviewed status; samples ni commercial Boju forms mbili na batches tatu tu kwa kila moja; different production years, storage conditions, processing methods na wider market samples hazijatest; baadhi ya important GC-IMS markers hazina exact chemical identification; na study haina comprehensive trained human sensory panel au consumer-perception validation.

Kwa hiyo, study inatoa strong analytical quality-assessment strategy ya DBJ/XBJ discrimination, lakini definite quality standard itahitaji broader sampling, multi-year studies, human sensory panels na advanced mass-spectrometric identification ya unknown markers.