Metabolomic Profiling as a Possible Reverse Engineering Tool for

Subscriber access provided by Fudan University

Article

Metabolomic profiling as a possible reverse engineering tool for estimating processing conditions of dry-cured hams Masahiro Sugimoto, Shinichi Obiya, Miku Kaneko, Ayame Enomoto, Mayu Honma, Masataka Wakayama, Tomoyoshi Soga, and Masaru Tomita J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b03844 • Publication Date (Web): 12 Dec 2016 Downloaded from http://pubs.acs.org on December 18, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 28

Journal of Agricultural and Food Chemistry

1 2

Metabolomic profiling as a possible reverse engineering tool for estimating

3

processing conditions of dry-cured hams

4 5

Masahiro Sugimotoa*, Shinichi Obiyab, Miku Kanekoa, Ayame Enomotoa,

6

Mayu Honmaa, Masataka Wakayamaa, Tomoyoshi Sogaa, and Masaru Tomitaa

7 8 9 10

a

Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan b Tohoku Ham, Tsuruoka Yamagata 997-0011, Japan

11 12 13 14

[email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], and [email protected]

15 16

19

*Corresponding author: Institute for Advanced Biosciences, Keio University, 246-2 Kakuganji, Tsuruoka, Yamagata 997-0052, Japan E-mail: [email protected] Tel: +81-235-29-0528

20

Fax: +81-235-29-0574

17 18

21 22 23

1

ACS Paragon Plus Environment


24 25 26 27 28 29 30 31 32 33 34 35 36

Abstract Dry-cured hams are popular among consumers. To increase the attractiveness of the product, objective analytical methods and algorithms to evaluate the relationship between observable properties and consumer acceptability are required. In this study, metabolomics, which is used for quantitative profiling of hundreds of small molecules, was applied to 12 kinds of dry-cured hams from Japan and Europe. In total, 203 charged metabolites, including amino acids, organic acids, nucleotides, and peptides, were successfully identified and quantified. Metabolite profiles were compared for the samples with different countries of origin and processing methods (e.g., smoking or use of a starter culture). Principal component analysis of the metabolite profiles with sensory properties revealed significant correlations for redness, homogeneity, and fat whiteness. This approach could be used to design new ham products by objective evaluation of various features.

37 38 39

Keywords Dry-cured ham, metabolomics, amino acid, peptide, organic acid, nucleic acid

40 41

2


Page 2 of 28

Page 3 of 28


42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77

Introduction Dry-cured ham is a popular product in Western countries. Although it is also popular in Japan, this meat product is relatively new in Japanese markets.1 To increase the value of ham products in Japan, features that are attractive to consumers need to be identified and the ingredients and procedure used for ham processing should be modified according to consumer preferences 2. However, the processing details for products obtained from other companies are often unknown, and analysis of chemical composition is one tool that can be used to estimate what conditions were used. Mass spectrometry (MS)-based molecule profiling techniques can be used to quantitatively catalogue various small molecules, including taste-active compounds 3-5. This technology can be used to analyze the chemical features of a food product. The results could contribute to reverse engineering of ham processing. Many studies have profiled taste-active compounds in dry-cured ham samples. Both volatile and non-volatile compounds are known to directly and indirectly contribute to the flavor, texture, and taste of ham. For example, amino acids provide sweetness and bitterness, while peptides provide flavor and bitterness, and these molecules also contribute to the synthesis of non-volatile compounds 6. Generally, volatile compounds have been profiled by gas chromatography/mass spectrometry, and a relatively small number of non-volatile ones showing similar chemical features have been profiled by liquid chromatography-mass spectrometry 6-15. Profiling of a wider range of molecules with sophisticated data analysis is required. Metabolomics provides profiling of metabolites, and has been used for the design of new foodstuffs by evaluating chemical quality 4, nutrition 16, and safety 17, as well as tracing the production origins 18. Capillary electrophoresis-mass spectrometry (CE-MS) is an analytical techniques used in metabolomics that enables simultaneous quantification of taste-active compounds, such as amino acids, peptide, organic acids, and nucleic acids, and also many hydrophilic compounds 19. We have used this method to analyze the sensory characters of rice wine 20, 21 and storage conditions of soybeans 22. A non-targeted approach profiling compounds with both known functions and those that have no defined function would contribute to the analysis of region- or nationality-specific acceptability and design of new products. The aim of this study was to analyze the different metabolite patterns among various dry-cured ham products with different processing histories obtained from multiple brands. Comprehensive profiling of charged metabolites in 12 kinds of dry-cured ham samples was conducted using CE-MS-based metabolomics. The processing procedure, ingredients, starter microorganism, and smoking were evaluated. 3



78 79 80 81 82

European hams from Italy, Spain, and German, were compared with several samples from Japan, including hams that are distributed commercially. We previously performed sensory evaluation for various types of dry-cured hams 23. Here, we conducted metabolomic analysis of the same ham samples and integrated the data to identify the chemical features that were characteristic of each ham.

83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102

Material and Methods Sensory evaluation of dry-cured ham samples Sensory evaluation scores were retrieved from the ref. 23. Briefly, evaluators included only consumers and no trained panelists, and samples of 12 hams (Nos. 1–12), including smoked (n = 5) and non-smoked (n = 7) hams (Table 1), were analyzed. Three of the hams were European hams, including a prosciutto (Galloni, Parma, Italy), a Jómon serrano (Espuña, Olot, Spain), and a Black Forest ham (Wein, Freudenstadt-Musbach, Germany), and three hams produced in Japan (Kyodo International Inc., Kanagawa, Japan and Tohoku Ham, Tsuruoka, Japan). The typical flow of ham processing was depicted at Figure 1A. For each ham sample, 10 features were evaluated for consumers acceptability, including three based on appearance, one based on flavor, three based on taste, two based on texture, and one on overall acceptance. All ham samples were evaluated at the same time. The ham samples were provided to the evaluators (n = 117) without revealing the brand, and were evaluated relative to a reference ham (No. 13). The reference ham was also included as ham No. 9 in a blind experiment to test the evaluators’ abilities (n =101 remained). Each feature of the hams was scored at a value between 1 and 5; the reference ham (No. 13) was given scores of 3 for all features. A score of 1 indicated that a feature was not preferable and a score of 5 indicated that a feature was preferable.

103 104 105 106 107 108 109 110

Chemical analysis The moisture, NaCl, pH, and water activity of each ham sample were measured by using an infrared aquameter (Kett Electric Laboratory, Tokyo, Japan), compact salt meter (Horiba, Kyoto, Japan), pH meter (Testo206-pH2 Testo, Yokohama, Japan), and a water activity meter (Pawkit, Decagon Devices, Pullman, WA), respectively. For these analysis, only lean meat parts far from edge were used for eliminate fat (examples were shown as labeled B1 to B3 at Figure 1B). All analyses were performed one time.

111 112 113

Sample preparation for metabolomic analysis Three small pieces (5 mm × 5 mm × 1 mm) for triplicate analyses were cut out 4


Page 4 of 28

Page 5 of 28


114 115

from each ham sample and immediately frozen. Preliminary measurements included data from both the edge (A1 to A3 at Figure 1B) and central parts (B1 to B3 at Figure

116

1B) of a ham sample (No. 7). For metabolite extraction, each frozen sample was

117 118

immediately plunged into methanol (500 µL) containing 20 µM each of methionine sulfone, D-camphor-10-sulfonic acid and 2-(N-morpholino)ethanesulfonic acid as

119

internal standards. Then, the sample was homogenized at 1500 rpm for 5 min using a

120

cell disruption device (Shake Master Neo, BMS, Tokyo, Japan). Subsequently, 500 µL of chloroform and 200 µL of Milli-Q (Millipore, Billerica, MA) water were added, and the solution was centrifuged at 4,600 ×g for 15 min at 4 °C. The upper aqueous layer

121 122 123 124 125 126 127

(300 µL) was centrifugally filtered (5-kDa cutoff filter, Millipore) at 9,100 ×g for 4 h at 4 °C to remove large molecules. The filtrate (300 µL) was lyophilized and dissolved in 50 µL of Milli-Q water containing a reference compound (200 µmol/L of 3-aminopyrrolidine and trimesate) before capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) analysis.

128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149

Metabolomics analysis using CE-TOFMS CE-TOFMS was performed with slight modification of an established method 24. Cation and anion analyses were performed using an Agilent CE system (G1600AX), an Agilent G1969A liquid chromatography mass selective detector TOF system, an Agilent 1100 series isocratic high performance liquid chromatography pump, a G3251A Agilent CE-MS adapter kit, and a G1607A Agilent CE-electrospray ionization (ESI)-MS sprayer kit (Agilent Technologies Deutschland GmbH & Co. KG, Waldbronn, Germany). The CE-MS adapter kit included a capillary cassette that facilitated thermostatic control of the capillary. The CE-ESI-MS sprayer kit simplified coupling of the CE system with the MS system and was equipped with an electrospray source. For system control and data acquisition, Agilent ChemStation software for CE (B02.00 and A10.02) and Agilent MassHunter software for TOF-MS (A.10.02 and B.02.00) were used. For anion analysis, the original Agilent SST316Ti stainless steel ESI needle (G1607-60041) was replaced with a platinum ESI needle (G1607-60041), which was passivated with 1 % formic acid and a 20 % aqueous solution of isopropanol at 80 ºC for 30 min. For cationic metabolite analysis using CE-TOFMS, sample separation was performed in fused silica capillaries (50 µm i.d. × 100 cm total length) filled with 1 mol/L formic acid as the running electrolyte. The capillary was flushed with formic acid (1 mol/L) for 20 min before the first use, and for 4 min before each sample injection. Sample solutions (approximately 3 nL) were injected at 50 mbar for 5 s and a voltage of 30 kV was applied. The capillary temperature was maintained at 20 °C and the 5



150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182

temperature of the sample tray was kept below 5 °C. The sheath liquid, which was a mixture of methanol and water (1:1, v/v) and 0.1 µmol/L hexakis(2,2-difluoroethoxy)phosphazene (Hexakis), was delivered at 10 µL/min. ESI-TOF-MS was conducted in positive ion mode. The capillary voltage was set at 4 kV, and the flow rate of nitrogen gas (heater temperature = 300 °C) was set at 7 psig. In TOF-MS, the fragmentor, skimmer, and OCT RF voltages were 75, 50, and 125 V, respectively. Automatic recalibration of each acquired spectrum was performed using the following reference standards:[13C isotopic ion of protonated methanol dimer (2MeOH + H)]+, m/z 66.0631; and [protonated Hexakis (M + H)]+, m/z 622.0290. Mass spectra were acquired at a rate of 1.5 cycles/s over a m/z range of 50–1,000. For anionic metabolite analysis using CE-TOFMS, a commercially available COSMO(+) capillary (50 µm × 103 cm, Nacalai Tesque, Kyoto, Japan), chemically coated with a cationic polymer, was used for separation. Ammonium acetate solution (50 mmol/L, pH 8.5) was used as the electrolyte for separation. Before the first use, the new capillary was flushed successively with the running electrolyte (pH 8.5), 50 mmol/L acetic acid (pH 3.4), and then the electrolyte again for 10 min each. Before each injection, the capillary was equilibrated for 2 min by flushing with 50 mmol/L acetic acid (pH 3.4) and then with the running electrolyte for 5 min. A sample solution (approximately 30 nL) was injected at 50 mbar for 30 s, and a voltage of −30 kV was applied. The capillary temperature was maintained at 20 °C and the sample tray was cooled below 5 °C. An Agilent 1100 series pump equipped with a 1:100 splitter was used to deliver 10 µL/min of 5 mM ammonium acetate in methanol/water (1:1, v/v), which contained 0.1 µmol/L Hexakis, to the CE interface. Here, it was used as a sheath liquid surrounding the CE capillary to provide a stable electrical connection between the tip of the capillary and the grounded electrospray needle. ESI-TOF-MS was conducted in negative ionization mode at a capillary voltage of 3.5 kV. For TOF-MS, the fragmentor, skimmer, and octpole RF voltages were set at 100, 50, and 200 V, respectively. The flow rate of the drying nitrogen gas (heater temperature = 300 °C) was maintained at 7 psig. Automatic recalibration of each acquired spectrum was performed using the following reference standards: [13C isotopic ion of deprotonated acetic acid dimer (2 CH3COOH–H)]–, m/z 120.03841; and [Hexakis deprotonated acetic acid (M + CH3COOH–H)]–, m/z 680.03554). Exact mass data were acquired at a rate of 1.5 spectra/s over a m/z range of 50–1,000.

183 184 185

Data Analysis Raw data were analyzed using our proprietary software, MasterHands 25, which follows 6


Page 6 of 28

Page 7 of 28


186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202

typical data processing flows, including the detection of all possible peaks, elimination of noise and redundant features, and generation of an aligned data matrix with annotated metabolite identities and relative areas (peak areas normalized to those of internal standards) 26. Concentrations were calculated using external standards based on relative area, that is, the area divided by the area of the internal standards. The concentration of each metabolite was normalized using the sample mass. Student’s t-test and one-way analysis of variance were used for comparisons of groups with two or greater than three members, respectively. Overall metabolomic profiles and sensory evaluation scores were accessed by principal component analyses (PCA), and Spearman ranked correlation was used to assess the correlation of metabolites in clustering analysis. PCA and clustering analysis utilize variable and correlation of each metabolite. Therefore, only metabolite data including few zero concentration, i.e. the detected concentration was under detection limit, for these analyses to eliminate the skewness of the analytical results. P < 0.05 was considered as statistically significant. Data visualization and analysis were conducted using XLSTAT (ver. 2014.1.04, Addinsoft, Paris, France), MeV TM4 18, and GraphPad Prism (ver 5.04, GraphPad Software Inc, La Jolla, CA).

203 204

Results

205

Overview of metabolomic profiles To determine the best method for obtaining representative samples of the ham, differences between metabolite concentrations for samples from the edge of the ham (n = 3), center of the ham (n = 3) (Figure 1B) were evaluated. A plot was constructed of the relative standard deviation against the metabolite concentration (Figure 2A). Preliminary measurements included data from both the edge and central parts of the

206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221

ham samples, and comparison of the edge (A), central (B), and edge + central data (A+B) resulted in the center data showed least deviation in the quantified values. Therefore, subsequent analyses were conducted using three pieces obtained from the center of each ham sample. The CE-MS analyses identified and quantified 203 metabolites, including amino acids, organic acids, peptides, nucleic acids, and charged metabolites (Figure S1). Among these metabolites, 98 were frequently observed in the samples. These metabolites were detected in more than 26 of the 36 samples (i.e., >70%), and used for PCA. The short distances between triplicates in the score plots indicated there was high reproducibility in the data for the overall patterns of quantified concentrations of the metabolites (Figure 2B). Consequently, the average values of the triplicate measurements were used 7



222 223 224 225 226 227 228 229 230 231

for the subsequent analyses. Hierarchical cluster analysis grouped the samples into three groups as follows: cluster a had a higher concentration of nucleic acids and glutamine (Gln) than the other clusters, cluster b contained all the organic acids except for lactate and the most of the amino acids and peptides, and cluster c contained several amino acids, lactate, and hypoxanthine (Figure 2C). The total concentrations for each metabolite group, including amino acids, peptides, nucleotides, and organic acids, in each sample were plotted (Figure 3). The patterns for each component metabolite were analyzed by PCA (Figure S2), and their relative balances are depicted on radar charts (Figures S3–S6). The concentration pattern for all quantified metabolites analyzed by PCA is depicted as a biplot (Figure 4)

232 233 234 235 236

Other parameters The moisture, pH, NaCl, and water activity for each ham are listed in Table 1. The sensory evaluation scores are given in Table S1 (n = 101). The correlation coefficients between principal components (PCs) and sensory scores are given in Table S2.

237 238 240

Discussion The aim of this study was to assess the potential of metabolomic profiling as a reverse engineering tool for ham processing. Here, we discuss the sample-specific profiles

241

based on metabolite groups and their relationships with processing conditions.

239

242 243 244 245 246 247 248 249 250

Profiles of amino acids, peptides, nucleotides, and organic acids There was significant (P < 0.05) variability among the ham samples for the total concentration of each type of metabolite (Figure 3). The total concentrations of amino acids for smoked hams (Nos. 1–6) were higher than hams prepared without smoking (Nos. 8–12). The processed ham (No. 7) had the lowest concentration of amino acids among all the ham samples (Figure 3A). Ham samples (Nos. 1–6) were ripen for longer than the other hams (Nos. 8–12), and the difference in the total concentration of amino

254

acids could be derived from this difference in ripening period rather than the process of smoking. The balance of individual amino acids was affected by the processing conditions (e.g., smoked or not smoked) for all hams except for No. 7 (Figure S3). The smoked hams had relatively lower aspartic acid contents and relatively higher glutamic acid contents compared with the hams prepared without smoking, regardless of the

255

country of origin.

251 252 253

256 257

The total peptides concentration showed similar trends to the amino acids, with 8


Page 8 of 28

Page 9 of 28


258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285

higher concentrations observed for the smoked hams (Nos. 1–6) than the hams prepared without smoking (Nos. 8–12) (Figure 3B). Two hams (Nos. 7 and 12) had significantly low concentrations of Gly-Gly (P = 0.011) (Figure S4). By contrast, the total concentrations of organic acids and nucleotides showed no differences between the smoked and non-smoked hams (Figures 3C and 3D). For the nucleotides, six samples (Nos. 1–6) among the smoked hams showed high hypoxanthine concentrations, and three samples (No. 8, 10, and 11) showed similar concentrations of hypoxanthine and inosine (Figure S6). For the organic acids, malate showed a unique pattern, with much higher concentrations in the Jómon serrano (No. 1), Lachsschinken (No. 9) Black Forest (No. 10), and prosciutto (No. 5) hams. The ham with the long ripening period (No. 6) had much higher organic acid concentrations. Hams No. 5 and No. 6 had the highest concentrations of malate among all the samples (Figure S6). Generally, the following processing occur during ham ripening: (1) protein hydrolysis increases peptide and amino acid contents; (2) activation of anaerobic metabolism induces adenosine triphosphate consumption, glycogen decomposition, lactate production, and pH reduction; and (3) the pH reduction inhibits anaerobic metabolism. Moisture retention is also reduced during ripening. For example, Japanese Serrano type ham (No. 3) had the highest total concentrations of amino acids (Figure 3A), peptides (Figure 3B), and lactate (Figure S6). This ham sample had the lowest pH (5.65) and second lowest moisture retention content (53%) among the hams, which could be attributed to its long ripening period. A remarkably high concentration of glutamic acid was observed in the processed ham (No. 7). The concentration of 2-oxoglutarate, which is derived from glutamic acid by glutamate dehydrogenase [EC:1.4.1.3], was also very high among all the samples (Figure S6). These results could indicate that glutamic acid has been added artificially to this ham. The concentration of inosine in sample No. 7 was also much higher than in the other samples (Figure S5), and this also supported the suggestion of artificial additives.. Therefore, this sample was excluded from the comparisons in the rest of the study.

286 287 288 289 290 291 292 293

Profiles of other metabolites Figure 4 shows a PCA biplot of all the quantified metabolites. Metabolites located far from the zero-point contributed more to sample-specific metabolomic profile characteristics of the ham. For example, metabolites showing large negative PC2 values (e.g., glycerophosphorylcholine and phosphorylcoline) were detected at low concentrations in sample No. 6 (long ripening period), which overall had a large positive PC2 value. On the biplot, creatinine, which has an oxidative effect in pork 27, 9



294 295 296 297

was located at the bottom right and creatine was located at the top left. Both these compounds contributed greatly to both PC1 and PC2. Taurine, which is known to be positively correlated with amino acid content 15, was located close to amino acid on the biplot, indicating similar concentration patterns among ham samples.

298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316

Comparison of metabolomic profiles between Japanese and European hams The biplot constructed using all the detected metabolites (Figure 4) showed both sample and metabolite-specific patterns. Samples from Italy (No. 2), Spain (No. 4), and German (No. 10) were located close to the negative area of PC2, while the Japanese samples were closely grouped. Comparison of PC2 between these three samples and the Japanese samples showed significant differences (P = 0.02). The Black Forest ham (No. 10) and Japan Rollschinken prepared with a starter culture (No. 12) showed similar profiles. The prosciutto sample from Italy (No. 2) showed higher concentrations of amino acids, peptides, organic acids, and nucleotides than the one from Japan (No. 6) (Figure 3). For the organic acid profiles (Figures 3D and S2D), the ham from Japan with a long ripening period (No. 6) had a very large PC2 value, indicated it had a unique profile (e.g., higher concentrations of cis-aconitate, citrate, and isocitrate and lower lactate and 3-methylbutanoate) compared with the other samples. A comparison between the Jómon serrano from Spain (No. 4) and that from Japan (No. 3) showed higher concentrations of amino acids, peptides, and organic acids were present in the Japanese ham (Figures 2A, 2B, and 2D). Sample No. 3 showed the highest PC1 among all the samples (Figures 4), and had a unique profile with higher lactate and lower fumarate concentrations than the other samples.

317 318 319 320 321 322 323 324 325 326 327 328 329

Effect of starter culture on metabolomic profiles Two Rollschinken ham samples from Japan that had been produced without smoking from thigh meat (Nos. 8 and 12) were selected for comparison of processing with and without a starter culture. The ham prepared with a starter culture (No. 12) had higher amino acid concentration (Figure 3A) and significantly higher lactate concentration (P = 0.017) than the ham prepared without starter culture (Figure S6). The effect of the starter culture was complicated, it reduced the pH (from 6.05 for No. 8 to 5.90 for No.12), which indicated acceleration of ripening, but increased moisture retention (moisture content 50.5% for No. 8 to 67.5% for No. 12) (Table 1). Among the overall metabolomic profiles, samples prepared without starter culture (No. 8) were located in close proximity to other smoked samples (No. 7, 9, and 11), while the ham prepared with starter culture (No. 12) was located in close proximity 10


Page 10 of 28

Page 11 of 28


330 331 332 333 334 335 336 337 338 339 340

to the German Black Forest ham (No. 10) (Figure 4). Metabolite group profiles were similar for the amino acids, peptides, and organic acids between samples No. 8 and No. 12 (Figures S2A, S2B and S2D), presumably because they had very similar origins and processing except for the use of starter culture. Only the nucleotide profiles showed large differences between these two hams (Figure S2C), and significantly lower inosine (P = 0.0026) and higher hypoxanthine (P = 0.0090) concentrations were observed in sample No. 8 than No. 12 (Figure S5). Among the organic acids, significantly lower malate (P = 0.00012) and fumarate (P = 8.5 × 10–5) concentrations were observed in sample No. 8 than No. 12 (Figure S6). All of the peptides showed unique patterns, and unlike the increased amino acid concentrations, significantly lower concentrations (P < 0.05) were observed for all peptides except for glycine-glycine (Gly-Gly) (Figure S4).

341 342 343 344 345 346 347 348 349 350

Summary of sensory evaluation scores The score of sensory evaluation tests were retrieved from the ref. 23 (Table S1) to explore the relationships between sensory aspects of the hams and the metabolite profiles. Total evaluation (defined as T1) and the sum of all of the other scores (defined as T2) showed high positive correlations (R = 0.91). Meat redness (R = 0.86), umami (R = 0.86), and sweetness (R = 0.83) showed high positive correlations, while flavor (R = 0.44) showed the most lowest correlation among the sensory aspects. Processed ham (No. 7), which had very high glutamic acid and inosinate concentrations, showed the highest T2 value, and was not included in further analysis.

351 352 353 354 355 356 357 358

Among the samples prepared without smoking, all sensory aspects for the rosciutto ham from Italy (No. 2) received higher scores than the prosciutto-style ham from Japan (No. 5). The taste and texture scores for the Jómon serrano ham from Spain (No. 4) were all higher than those of the Jómon serrano-style hams. The Jómon serrano-style ham from Japan (No. 3), had the highest concentrations of amino acids, peptides, and organic acids among the samples prepared without smoking, and showed

362

no distinguishing scores among the same type of the hams. Among the samples prepared with smoking, the German Black Forest ham (No. 10) scored the highest for meat redness, fat whiteness, and flavor. The Rohschinnken hams from Japan prepared with and without starter culture (No. 8 and No. 12) scored higher for texture than the other samples. In these two samples, all variables except for fat whiteness and

363

sweetness were higher in No. 8 than No. 12.

359 360 361

364 365

Relationship between metabolites and sensory evaluation scores 11



366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392

To understand the relationship between acceptability and metabolomic profiles, correlation coefficients between sensory evaluation scores and PC values were calculated (Table S2) for each metabolite group (Figures 3 and S1). The appearance and taste were significantly (P < 0.05) correlated with the PC values. For example, the meat redness and PC2 of the nucleotides were negatively correlated (R = −0.718), whereas meat homogeneity was positively correlated with PC1 of all metabolites (R = 0.645), amino acids (R = 0.664), peptides (R = 0.791), and nucleotides (R = 0.627). Fat whiteness was positively correlated with PC1 of all metabolites (R = 0.745) and nucleotides (R = 0.673). Sweetness was negatively correlated with PC2 of all metabolites (R = −0.682), and umami was negatively correlated with PC2 of all metabolites (R = −0.644) and nucleotides (R = −0.703). By contrast, flavor, texture, and overall evaluation were not significantly correlated with any PC values. For the PCA plot constructed using all metabolites (Figure 4), among the ham samples produced without smoking (No. 1–6), prosciutto from Italy (No. 2) and Jómon serrano from Spain (No. 4) were located in close proximity to each other for both PC1 and PC2 axis. This indicates that these samples have similar metabolomic profiles. By contrast, Jómon serrano from Japan (No. 3) was located on the right of the plot. Prosciutto from Japan (No. 5) was located close to samples No. 2 and No. 4, which had high saltiness scores (≥3) compared with the other samples (

Metabolomic Profiling as a Possible Reverse Engineering Tool for

Recommend Documents