{"id":1947,"date":"2026-08-23T20:43:53","date_gmt":"2026-08-23T20:43:53","guid":{"rendered":"https:\/\/lean-app.com\/?p=1947"},"modified":"2026-09-05T10:39:09","modified_gmt":"2026-09-05T10:39:09","slug":"etude-base-donnees-calories","status":"publish","type":"post","link":"https:\/\/lean-app.com\/de\/etude-base-donnees-calories\/","title":{"rendered":"Wir haben 857 655 in Frankreich verkaufte Produkte analysiert: was dir dein Kalorienscanner nicht sagt"},"content":{"rendered":"<script data-wpmeteor-nooptimize=\"true\" type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Dataset\",\n  \"name\": \"Coh\u00e9rence calorique des produits alimentaires vendus en France (OpenFoodFacts, ao\u00fbt 2026)\",\n  \"description\": \"Agr\u00e9gats issus de l'analyse de 857 655 produits vendus en France depuis le dump public OpenFoodFacts du 23 ao\u00fbt 2026 : compl\u00e9tude des fiches et coh\u00e9rence entre calories d\u00e9clar\u00e9es et calories recalcul\u00e9es depuis les macronutriments (facteurs Atwater, r\u00e8glement INCO 1169\/2011).\",\n  \"url\": \"https:\/\/lean-app.com\/etude-base-donnees-calories\/\",\n  \"creator\": {\"@type\": \"Organization\", \"name\": \"L'\u00e9quipe Lean\", \"url\": \"https:\/\/lean-app.com\/\"},\n  \"identifier\": [\"https:\/\/doi.org\/10.5281\/zenodo.22284416\", \"https:\/\/doi.org\/10.6084\/m9.figshare.33432244\"],\n  \"sameAs\": [\"https:\/\/zenodo.org\/records\/22284416\", \"https:\/\/figshare.com\/articles\/preprint\/33432244\", \"https:\/\/osf.io\/tczvj\/\", \"https:\/\/www.wikidata.org\/wiki\/Q141267884\"],\n  \"isBasedOn\": \"https:\/\/world.openfoodfacts.org\/data\",\n  \"license\": \"https:\/\/opendatacommons.org\/licenses\/odbl\/1-0\/\",\n  \"temporalCoverage\": \"2026-08-23\",\n  \"spatialCoverage\": \"France\",\n  \"variableMeasured\": [\"energy-kcal_100g\", \"proteins_100g\", \"carbohydrates_100g\", \"fat_100g\", \"fiber_100g\", \"alcohol_100g\", \"polyols_100g\"]\n}\n<\/script>\n<script data-wpmeteor-nooptimize=\"true\" type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\"@type\": \"Question\", \"name\": \"Pourquoi analyser OpenFoodFacts et pas USDA ?\",\n     \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"Pour les produits \u00e0 code-barres vendus en France, la source de r\u00e9f\u00e9rence des trackers est OpenFoodFacts. USDA couvre les aliments bruts et le march\u00e9 am\u00e9ricain. L'\u00e9tude porte sur ce que renvoie r\u00e9ellement un scan en France.\"}},\n    {\"@type\": \"Question\", \"name\": \"Faut-il arr\u00eater de scanner ses aliments ?\",\n     \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"Non. Le scan reste le moyen le plus rapide de logger. La bonne pratique : v\u00e9rifier que les macros affich\u00e9es sont coh\u00e9rentes avec les calories. Si prot\u00e9ines, glucides et lipides ne collent pas aux kcal, la fiche est fausse.\"}},\n    {\"@type\": \"Question\", \"name\": \"Les applications corrigent-elles ces erreurs ?\",\n     \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"La base OpenFoodFacts est collaborative et vit en continu : 4 des 6 pires fiches identifi\u00e9es dans le dump avaient d\u00e9j\u00e0 \u00e9t\u00e9 corrig\u00e9es en ligne au moment de l'\u00e9tude. Mais une application qui a synchronis\u00e9 une fiche fausse peut la garder en cache longtemps apr\u00e8s la correction.\"}},\n    {\"@type\": \"Question\", \"name\": \"La base de Lean est-elle diff\u00e9rente ?\",\n     \"acceptedAnswer\": {\"@type\": \"Answer\", \"text\": \"Lean croise la base USDA pour les aliments bruts et OpenFoodFacts pour les produits \u00e0 code-barres, et propose le scan photo IA qui estime l'assiette sans d\u00e9pendre d'une fiche. Surtout, Lean concentre la pr\u00e9cision l\u00e0 o\u00f9 l'erreur est invisible : le calcul de la d\u00e9pense (TDEE = BMR + NEAT + EAT + TEF).\"}}\n  ]\n}\n<\/script>\n<style id=\"lvm-shell-styles\">#lvm-shell{\n  --bg:#FFFFFF; --paper:#F7F6F2; --paper-2:#F1EFE7;\n  --ink:#0E0E10; --ink-2:#1D1D1F; --muted:#6E6E73; --dim:#86868B;\n  --rule:#E8E6DF; --rule-soft:#EFEDE5;\n  --pink:#FF2D6E; --pink-soft:rgba(255,45,110,0.06);\n  --mfp:#6ABF6C;\n  --green:#0F8F5C; --red:#D02E2E; --amber:#C8A019;\n  --font-display:-apple-system,\"SF Pro Display\",system-ui,\"Helvetica Neue\",sans-serif;\n  --font-text:-apple-system,\"SF Pro Text\",system-ui,sans-serif;\n  --font-mono:ui-monospace,\"SF Mono\",Menlo,Consolas,monospace;\n}\n#lvm-shell *{box-sizing:border-box;-webkit-text-size-adjust:100%}\n#lvm-shell, #lvm-shell{margin:0;padding:0;background:var(--bg);color:var(--ink-2);font-family:var(--font-text);font-size:17px;line-height:1.7;-webkit-font-smoothing:antialiased}\n#lvm-shell 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strong{display:block;color:var(--ink);margin-top:4px;font-family:var(--font-display);font-size:14px;font-weight:500;letter-spacing:-.01em;text-transform:none}\n\n#lvm-shell .duo-row{display:grid;grid-template-columns:repeat(2,1fr);gap:20px;margin:18px 0 0}\n#lvm-shell .duo-row .mini-phone{max-width:180px}\n\n#lvm-shell .method{display:grid;grid-template-columns:1fr 1.4fr;gap:36px;align-items:center;margin:42px 0}\n#lvm-shell .method.flip{grid-template-columns:1.4fr 1fr}\n#lvm-shell .method.flip .m-phone{order:2}\n#lvm-shell .method .m-tag{font-family:var(--font-mono);font-size:11px;font-weight:600;text-transform:uppercase;letter-spacing:.08em;color:var(--pink);margin-bottom:8px}\n#lvm-shell .method h3{margin-top:0}\n#lvm-shell .method p{font-size:16px;color:var(--muted);line-height:1.7}\n\n#lvm-shell .cta-band{margin:40px 0;padding:26px 28px;background:var(--paper);border-radius:16px;display:flex;align-items:center;justify-content:space-between;gap:20px;flex-wrap:wrap;border:1px solid var(--rule-soft)}\n#lvm-shell .cta-band .l{font-family:var(--font-display);font-size:18px;line-height:1.35;font-weight:500;color:var(--ink);flex:1;min-width:240px;letter-spacing:-.01em}\n#lvm-shell .cta-band .stores{display:flex;gap:10px;align-items:center}\n#lvm-shell .cta-band .stores a{line-height:0;transition:transform .15s}\n#lvm-shell .cta-band .stores a:hover{transform:translateY(-2px)}\n#lvm-shell .cta-band .stores img{height:42px;width:auto;border-radius:9px}\n\n#lvm-shell .pyramid{margin:30px auto;max-width:440px}\n#lvm-shell .pyramid .level{margin:6px auto;padding:13px 18px;border-radius:14px;color:#fff;font-family:var(--font-display);font-weight:500;font-size:15px;letter-spacing:-.01em;display:flex;justify-content:space-between;align-items:center;box-shadow:0 6px 18px rgba(0,0,0,.06)}\n#lvm-shell .pyramid .level .k{font-family:var(--font-mono);font-size:10px;text-transform:uppercase;letter-spacing:.08em;opacity:.75}\n#lvm-shell .pyramid .l1{background:#0E0E10;width:100%}\n#lvm-shell .pyramid .l2{background:#1D1D1F;width:84%}\n#lvm-shell .pyramid .l3{background:#3a3a3c;width:68%}\n#lvm-shell .pyramid .l4{background:var(--pink);width:52%}\n#lvm-shell .pyramid-cap{text-align:center;font-size:13px;color:var(--muted);margin-top:14px}\n\n\/* Section 7 honnetete : scorecard horizontal bars *\/\n#lvm-shell .scorecard{margin:30px 0 10px;border:1px solid var(--rule);border-radius:20px;padding:28px 26px;background:#fff}\n#lvm-shell .scorecard-head{display:grid;grid-template-columns:1.4fr 1fr 1fr;column-gap:28px;align-items:center;padding-bottom:18px;margin-bottom:8px;border-bottom:1px solid var(--rule-soft)}\n#lvm-shell .scorecard-head .h-crit{font-family:var(--font-mono);font-size:11px;font-weight:500;text-transform:uppercase;letter-spacing:.08em;color:var(--muted)}\n#lvm-shell .scorecard-head .h-brand{display:flex;align-items:center;gap:8px;font-family:var(--font-display);font-size:14px;font-weight:600;color:var(--ink)}\n#lvm-shell .scorecard-head .h-brand img{width:22px;height:22px;border-radius:5px;object-fit:cover}\n#lvm-shell .scorecard-row{display:grid;grid-template-columns:1.4fr 1fr 1fr;column-gap:28px;align-items:center;padding:14px 0;border-bottom:1px solid var(--rule-soft)}\n#lvm-shell .scorecard-row:last-child{border-bottom:0}\n#lvm-shell .scorecard-row .crit{font-size:14px;color:var(--ink);font-weight:500;padding-right:14px}\n#lvm-shell .scorecard-row .bar{display:flex;flex-direction:row-reverse;align-items:center;gap:10px}\n#lvm-shell .scorecard-row .bar .b{flex:1;height:8px;border-radius:99px;background:var(--rule-soft);overflow:hidden;position:relative}\n#lvm-shell .scorecard-row .bar .b > i{display:block;height:100%;border-radius:99px;transition:width 1s cubic-bezier(.22,.61,.36,1)}\n#lvm-shell .scorecard-row .bar.lean .b > i{background:var(--pink)}\n#lvm-shell .scorecard-row .bar.mfp .b > i{background:var(--mfp)}\n#lvm-shell .scorecard-row .bar .v{font-family:var(--font-mono);font-size:12px;font-weight:600;color:var(--ink);min-width:32px;text-align:left}\n\n\/* Section 8 pour qui : persona checklist *\/\n#lvm-shell .persona{margin:28px 0 10px;display:grid;grid-template-columns:1fr;gap:14px}\n#lvm-shell .persona-it{display:grid;grid-template-columns:54px 1fr;gap:16px;padding:22px 24px;background:#fff;border:1px solid var(--rule);border-radius:18px;align-items:center}\n#lvm-shell .persona-it.match{background:var(--pink-soft);border-color:rgba(255,45,110,.25)}\n#lvm-shell .persona-it .pic{width:54px;height:54px;border-radius:50%;display:flex;align-items:center;justify-content:center;background:var(--rule-soft);position:relative;font-family:var(--font-mono);font-size:13px;font-weight:600;color:var(--ink)}\n#lvm-shell .persona-it.match .pic{background:var(--pink);color:#fff}\n#lvm-shell .persona-it .pic svg{width:24px;height:24px}\n#lvm-shell .persona-it h4{margin:0 0 4px;font-size:17px;letter-spacing:-.01em}\n#lvm-shell .persona-it p{margin:0;font-size:14px;color:var(--muted);line-height:1.55}\n#lvm-shell .persona-it.match h4{color:var(--ink)}\n\n\/* Section 9 migration : timeline steps *\/\n#lvm-shell .steps{display:grid;grid-template-columns:repeat(5,1fr);gap:14px;margin:28px 0;position:relative}\n#lvm-shell .steps::before{content:\"\";position:absolute;top:14px;left:7px;right:calc(20% - 18px);height:1px;background:linear-gradient(90deg,var(--pink) 0%,var(--rule-soft) 100%);z-index:0}\n#lvm-shell .step{position:relative;padding-top:24px;z-index:1}\n#lvm-shell .step::before{content:\"\";position:absolute;top:8px;left:0;width:14px;height:14px;border-radius:50%;background:var(--pink);border:3px solid #fff;box-shadow:0 0 0 1px var(--rule)}\n#lvm-shell .step .sn{font-family:var(--font-mono);font-size:11px;color:var(--pink);font-weight:600;letter-spacing:.08em}\n#lvm-shell .step h4{margin:6px 0 6px;font-size:15px;letter-spacing:-.01em}\n#lvm-shell .step p{font-size:13px;color:var(--muted);line-height:1.5;margin:0}\n\n\/* Section 10 debloque : feature stack numbered XL *\/\n#lvm-shell .feat-stack{margin:30px 0 10px;border-top:1px solid var(--rule)}\n#lvm-shell .feat-it{display:grid;grid-template-columns:auto 1fr auto;gap:24px;padding:26px 0;border-bottom:1px solid var(--rule);align-items:center}\n#lvm-shell .feat-it .fn{font-family:var(--font-display);font-size:48px;font-weight:600;color:var(--pink);line-height:1;letter-spacing:-.04em;width:74px}\n#lvm-shell .feat-it .ft{font-family:var(--font-display);font-size:22px;font-weight:600;color:var(--ink);letter-spacing:-.015em;line-height:1.25;margin-bottom:6px}\n#lvm-shell .feat-it .fd{font-size:15px;color:var(--muted);line-height:1.55;margin:0}\n#lvm-shell .feat-it .fc{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;letter-spacing:.08em;color:var(--muted);font-weight:500}\n#lvm-shell .feat-it:last-child{border-bottom:0}\n\n#lvm-shell .faq{margin:22px 0}\n#lvm-shell .faq details{border-bottom:1px solid var(--rule);padding:20px 0}\n#lvm-shell .faq details:first-of-type{border-top:1px solid var(--rule)}\n#lvm-shell .faq summary{cursor:pointer;list-style:none;display:flex;justify-content:space-between;align-items:center;gap:18px;font-family:var(--font-display);font-size:20px;font-weight:500;letter-spacing:-.015em;color:var(--ink)}\n#lvm-shell .faq summary::-webkit-details-marker{display:none}\n#lvm-shell .faq summary::after{content:\"+\";font-size:24px;color:var(--muted);font-weight:300;line-height:1;transition:transform .25s, color .25s}\n#lvm-shell .faq details[open] summary::after{transform:rotate(45deg);color:var(--pink)}\n#lvm-shell .faq details[open] summary{color:var(--pink)}\n#lvm-shell .faq .ans{margin-top:14px;font-size:16px;color:var(--muted);line-height:1.65}\n\n#lvm-shell .get-band{background:var(--paper-2);border-radius:24px;padding:48px 36px;margin:60px 0 40px;text-align:center}\n#lvm-shell .get-band .kicker{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;color:var(--pink);font-weight:600;letter-spacing:.1em;margin-bottom:14px}\n#lvm-shell .get-band h3{font-size:36px;margin:0 0 14px;letter-spacing:-.025em}\n#lvm-shell .get-band p{font-size:16px;color:var(--muted);max-width:480px;margin:0 auto 26px}\n#lvm-shell .get-band .stores{display:flex;justify-content:center;gap:14px;flex-wrap:wrap}\n#lvm-shell .get-band .stores a{line-height:0;transition:transform .15s}\n#lvm-shell .get-band .stores a:hover{transform:translateY(-3px)}\n#lvm-shell .get-band .stores img{height:60px;width:auto;border-radius:11px}\n\n#lvm-shell .sources{font-size:14px;color:var(--muted);line-height:1.7}\n#lvm-shell .sources ol{padding-left:22px}\n#lvm-shell .sources li{margin-bottom:8px}\n\n#lvm-shell footer{padding:50px 0 60px;border-top:1px solid var(--rule);margin-top:40px}\n#lvm-shell footer .row{display:flex;justify-content:space-between;align-items:center;gap:18px;flex-wrap:wrap}\n#lvm-shell footer .kicker{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;letter-spacing:.08em;color:var(--pink);font-weight:600}\n#lvm-shell footer p{font-size:13px;color:var(--muted);margin:8px 0 0}\n#lvm-shell footer .stores{display:flex;gap:8px}\n#lvm-shell footer .stores img{height:34px;width:auto;border-radius:6px}\n\n#lvm-shell .rev{opacity:0;transform:translateY(12px);transition:opacity .8s cubic-bezier(.22,.61,.36,1),transform .8s cubic-bezier(.22,.61,.36,1)}\n#lvm-shell .rev.on{opacity:1;transform:translateY(0)}\n@media (prefers-reduced-motion:reduce){#lvm-shell .rev{transition:none;opacity:1;transform:none}}\n\n@media (max-width:760px){\n  #lvm-shell .nav-row{padding:8px 18px;gap:8px}\n  #lvm-shell .nav-link{display:none}\n  #lvm-shell .nav-stores img{height:24px}\n  #lvm-shell .wrap{padding:0 22px}\n  #lvm-shell .hero{padding:34px 0 0}\n  #lvm-shell h1{font-size:46px;letter-spacing:-.035em}\n  #lvm-shell h1 .alt{font-size:.55em;margin-top:10px}\n  #lvm-shell .dek{font-size:20px}\n  #lvm-shell .hero-stores img{height:42px}\n  #lvm-shell .hero-bottom{grid-template-columns:1fr;gap:28px;margin:30px 0 40px;padding-top:24px;align-items:stretch}\n  #lvm-shell .phone-wrap{order:-1}\n  #lvm-shell .phone{width:240px}\n  #lvm-shell .tap-hint.desktop{display:none}\n  #lvm-shell .tap-hint.mobile{display:block;position:relative;left:auto;top:auto;text-align:center;margin:0 auto 10px;width:100%}\n  #lvm-shell .tap-hint.mobile .th-arrow{position:relative;display:block;margin:6px auto 0;width:34px;height:34px;transform:none;color:var(--pink)}\n  #lvm-shell .snippet{padding:24px 22px}\n  #lvm-shell .snippet p{font-size:18px}\n  #lvm-shell section{padding:48px 0}\n  #lvm-shell h2{font-size:34px;letter-spacing:-.03em}\n  #lvm-shell h3{font-size:24px}\n  #lvm-shell .section-label{margin-bottom:22px}\n  #lvm-shell .statement{padding:24px 0;margin:32px 0}\n  #lvm-shell .statement .num{font-size:44px}\n  #lvm-shell .statement .lbl{font-size:19px}\n  #lvm-shell .fig{padding:20px 14px 14px;border-radius:16px}\n  #lvm-shell .cv-wrap{height:310px}\n  #lvm-shell .method{grid-template-columns:1fr;gap:20px}\n  #lvm-shell .method.flip{grid-template-columns:1fr}\n  #lvm-shell .method.flip .m-phone{order:0}\n  #lvm-shell .mini-row{grid-template-columns:repeat(3,1fr);gap:10px}\n  #lvm-shell .mini-phone{padding:3px;border-radius:18px;border-width:1px;max-width:110px}\n  #lvm-shell .mini-phone .notch{width:42px;height:11px;border-radius:0 0 8px 8px}\n  #lvm-shell .mini-phone .scr{border-radius:15px}\n  #lvm-shell .mini-cap{font-size:10px}\n  #lvm-shell .mini-cap strong{font-size:13px}\n  #lvm-shell .duo-row{grid-template-columns:repeat(2,1fr);gap:12px}\n  #lvm-shell .duo-row .mini-phone{max-width:130px}\n  #lvm-shell .steps{grid-template-columns:1fr;gap:18px}\n  #lvm-shell .steps::before{display:none}\n  #lvm-shell .step{padding-top:0;padding-left:24px}\n  #lvm-shell .step::before{top:6px;left:0}\n  #lvm-shell .table-row{grid-template-columns:1.4fr .9fr .9fr}\n  #lvm-shell .table-row > .crit{padding:13px 12px;font-size:13px}\n  #lvm-shell .table-row > .cell{padding:13px 10px;font-size:12px;gap:8px}\n  #lvm-shell .table-row.head > div{padding:14px 12px;font-size:10px;gap:7px}\n  #lvm-shell .table-row.head .brand-cell img{width:20px;height:20px}\n  #lvm-shell .get-band{padding:36px 22px;border-radius:18px;margin:40px 0 30px}\n  #lvm-shell .get-band h3{font-size:28px}\n  #lvm-shell .get-band .stores img{height:50px}\n  #lvm-shell .cta-band{padding:22px;gap:14px}\n  #lvm-shell .cta-band .l{font-size:16px;min-width:0}\n  #lvm-shell .cta-band .stores img{height:38px}\n  #lvm-shell .faq summary{font-size:18px;gap:14px}\n  #lvm-shell .pyramid{max-width:100%}\n  #lvm-shell .pyramid .level{padding:11px 14px;font-size:14px}\n  #lvm-shell .scorecard{padding:20px 16px;border-radius:16px}\n  #lvm-shell .scorecard-head{grid-template-columns:1.2fr 1fr 1fr;column-gap:14px}\n  #lvm-shell .scorecard-head .h-brand{font-size:12px;gap:5px}\n  #lvm-shell .scorecard-head .h-brand img{width:18px;height:18px}\n  #lvm-shell .scorecard-row{grid-template-columns:1.2fr 1fr 1fr;column-gap:14px;padding:12px 0}\n  #lvm-shell .scorecard-row .crit{font-size:13px;padding-right:8px}\n  #lvm-shell .scorecard-row .bar{gap:6px}\n  #lvm-shell .scorecard-row .bar .v{font-size:11px;min-width:26px}\n  #lvm-shell .persona-it{grid-template-columns:44px 1fr;gap:12px;padding:16px 16px;border-radius:14px}\n  #lvm-shell .persona-it .pic{width:44px;height:44px;font-size:12px}\n  #lvm-shell .persona-it h4{font-size:15px}\n  #lvm-shell .persona-it p{font-size:13px}\n  #lvm-shell .feat-it{grid-template-columns:auto 1fr;gap:14px;padding:20px 0}\n  #lvm-shell .feat-it .fn{font-size:36px;width:54px}\n  #lvm-shell .feat-it .ft{font-size:18px}\n  #lvm-shell .feat-it .fd{font-size:13px}\n  #lvm-shell .feat-it .fc{display:none}\n}\n@media (max-width:480px){\n  #lvm-shell .phone-tabs{gap:5px}\n  #lvm-shell .phone-tabs button{padding:5px 8px;font-size:10px}\n  #lvm-shell .nav-stores{gap:4px}\n  #lvm-shell .nav-stores img{height:22px}\n  #lvm-shell .hero-stores img{height:40px}\n  #lvm-shell .crumb{font-size:12px}\n  #lvm-shell .table-row{grid-template-columns:1.3fr .85fr .85fr}\n  #lvm-shell .table-row > .crit{padding:11px 9px;font-size:12px}\n  #lvm-shell .table-row > .cell{padding:11px 8px;font-size:11px;gap:6px}\n  #lvm-shell .table-row.head > div{padding:11px 9px;font-size:9px;gap:5px}\n}\n\/* === Etude OFF === *\/\n#lvm-shell .bignum{font-family:var(--font-display);font-weight:600;font-size:88px;letter-spacing:-.045em;line-height:1;color:var(--pink);margin:8px 0 4px;font-variant-numeric:tabular-nums}\n#lvm-shell .bignum small{display:block;font-size:20px;color:var(--muted);font-weight:500;letter-spacing:-.01em;margin-top:10px;line-height:1.4}\n#lvm-shell .stat-grid{display:grid;grid-template-columns:repeat(3,1fr);gap:14px;margin:30px 0}\n#lvm-shell .stat-card{background:var(--paper);border:1px solid var(--rule-soft);border-radius:16px;padding:22px 20px}\n#lvm-shell .stat-card .n{font-family:var(--font-display);font-weight:600;font-size:34px;letter-spacing:-.03em;color:var(--ink);font-variant-numeric:tabular-nums}\n#lvm-shell .stat-card .n.pink{color:var(--pink)}\n#lvm-shell .stat-card .t{font-size:13.5px;color:var(--muted);margin-top:6px;line-height:1.5}\n#lvm-shell .metho{background:var(--paper);border:1px solid var(--rule-soft);border-left:4px solid var(--pink);border-radius:0 16px 16px 0;padding:24px 26px;margin:30px 0}\n#lvm-shell .metho h3{font-family:var(--font-display);font-size:17px;font-weight:600;margin:0 0 12px;letter-spacing:-.01em}\n#lvm-shell .metho ul{margin:0;padding-left:20px;font-size:15px;color:var(--muted);line-height:1.7}\n#lvm-shell .metho li{margin:4px 0}\n#lvm-shell .metho code{font-family:var(--font-mono);font-size:13.5px;background:var(--paper-2);padding:1px 6px;border-radius:5px;color:var(--ink-2)}\n#lvm-shell .prod-cards{display:grid;gap:14px;margin:28px 0}\n#lvm-shell .prod-card{display:flex;align-items:center;gap:18px;background:var(--paper);border:1px solid var(--rule-soft);border-radius:16px;padding:18px 22px;flex-wrap:wrap}\n#lvm-shell .prod-card .who{flex:1;min-width:200px}\n#lvm-shell .prod-card .who b{font-family:var(--font-display);font-size:16.5px;letter-spacing:-.01em;color:var(--ink);display:block}\n#lvm-shell .prod-card .who span{font-size:13px;color:var(--muted)}\n#lvm-shell .prod-card .vals{display:flex;align-items:center;gap:14px;font-variant-numeric:tabular-nums}\n#lvm-shell .prod-card .fiche{font-family:var(--font-display);font-size:24px;font-weight:600;color:var(--ink);letter-spacing:-.02em}\n#lvm-shell .prod-card .fleche{color:var(--dim);font-size:17px}\n#lvm-shell .prod-card .reel{font-family:var(--font-display);font-size:24px;font-weight:600;color:var(--pink);letter-spacing:-.02em}\n#lvm-shell .prod-card .unit{font-size:11.5px;color:var(--dim);display:block;text-align:center;font-family:var(--font-mono);text-transform:uppercase;letter-spacing:.05em;margin-top:2px}\n@media (max-width:640px){\n  #lvm-shell .bignum{font-size:56px}\n  #lvm-shell .bignum small{font-size:16px}\n  #lvm-shell .stat-grid{grid-template-columns:1fr}\n  #lvm-shell .prod-card .fiche, #lvm-shell .prod-card .reel{font-size:20px}\n  #lvm-shell .cta-band{flex-direction:column;align-items:flex-start}\n  #lvm-shell .cta-band .l{min-width:100%;flex:none}\n}\n<\/style>\n\n<style id=\"lvm-collision-reset\">\nbody.postid-1947 #lvm-shell .hero{display:block!important;align-items:initial!important;justify-content:initial!important;text-align:left!important;flex-direction:initial!important;padding:54px 0 0!important}\nbody.postid-1947 #lvm-shell .wrap,\nbody.postid-1947 #lvm-shell 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src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/>\n      <span>Lean<\/span>\n    <\/a>\n    <span class=\"nav-spacer\"><\/span>\n    <a class=\"nav-link\" href=\"https:\/\/lean-app.com\/de\/tdee-calculator\/\">TDEE-Rechner<\/a>\n    <div class=\"nav-stores\">\n      <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\" aria-label=\"Im App Store laden\">\n        <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n      <\/a>\n      <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\" aria-label=\"Verf\u00fcgbar bei Google Play\">\n        <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n      <\/a>\n    <\/div>\n  <\/div>\n<\/header>\n\n<main class=\"wrap\">\n\n<section class=\"hero\" aria-labelledby=\"title\">\n  <div class=\"crumb\"><a href=\"https:\/\/lean-app.com\/de\/\">Startseite<\/a> &nbsp;\/&nbsp; Studie Kalorien-Datenbank<\/div>\n  <div class=\"eyebrow\">Original-Studie &middot; \u00d6ffentliche Daten<\/div>\n  <h1 id=\"title\">857&#8239;655 Produkte analysiert.\n    <span class=\"alt\">Was dein Kalorien-Scanner dir nicht sagt.<\/span>\n  <\/h1>\n  <p class=\"dek\">Jeder Barcode-Scan in Frankreich fragt dieselbe Datenbank ab. Wir haben sie komplett analysiert. Mehr als jeder dritte Scan liefert fehlende, unvollst\u00e4ndige oder inkonsistente Daten.<\/p>\n  <div class=\"byline\">\n    <img class=\"by-logo\" src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"26\" height=\"26\" loading=\"lazy\" decoding=\"async\" \/>\n    <span>Von <strong>Das Lean-Team<\/strong> &middot; ver\u00f6ffentlicht am 24. August 2026 &middot; 7 Min. Lesezeit<\/span>\n  <\/div>\n  <div class=\"hero-stores\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\" aria-label=\"Lean im App Store laden\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"T\u00e9l\u00e9charger sur l'App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\" aria-label=\"Lean bei Google Play laden\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Disponible sur Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"s1\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">01<\/span><\/div>\n  <h2 id=\"s1\">Warum diese Studie<\/h2>\n  <p>Wenn du in einem Kalorien-Tracker in Frankreich einen Barcode scannst, ob MyFitnessPal, Yazio, Lifesum oder Lean, kommen die Daten aus mehr oder weniger derselben Quelle: <a href=\"https:\/\/world.openfoodfacts.org\/\" target=\"_blank\" rel=\"noopener\">OpenFoodFacts<\/a>, einer bemerkenswerten franz\u00f6sischen kollaborativen Datenbank unter offener ODbL-Lizenz. Millionen Produkteintr\u00e4ge, gepflegt von freiwilligen Beitragenden.<\/p>\n  <p>Eine kollaborative Datenbank ist St\u00e4rke und Grenze zugleich: Jeder kann einen Eintrag anlegen oder \u00e4ndern, und niemand pr\u00fcft systematisch. Niemand hatte je quantifiziert, wie zuverl\u00e4ssig das ist, was dein Scan zur\u00fcckliefert. Also haben wir den kompletten \u00f6ffentlichen Dump vom 23. August 2026 heruntergeladen und jedes in Frankreich verkaufte Produkt getestet.<\/p>\n  <div class=\"stat-grid\">\n    <div class=\"stat-card\"><div class=\"n\">4 535 553<\/div><div class=\"t\">Produkte in der weltweiten Datenbank am 23. August 2026<\/div><\/div>\n    <div class=\"stat-card\"><div class=\"n\">1 265 726<\/div><div class=\"t\">Produkte, gelistet als in Frankreich verkauft<\/div><\/div>\n    <div class=\"stat-card\"><div class=\"n pink\">857 655<\/div><div class=\"t\">analysierbare Produkte: Kalorien und alle 3 Makros ausgef\u00fcllt<\/div><\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"s2\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">02<\/span><\/div>\n  <h2 id=\"s2\">Die Methode: jeder Eintrag gegen sich selbst getestet<\/h2>\n  <p>Kein Labor n\u00f6tig, um einen falschen Eintrag zu erkennen: Man konfrontiert ihn einfach mit sich selbst. Die Kalorien eines Lebensmittels lassen sich aus seinen Makron\u00e4hrstoffen ableiten, mit den Atwater-Faktoren, genau denen, die die europ\u00e4ische INCO-Verordnung den Herstellern vorschreibt: 4&nbsp;kcal pro Gramm Protein, 4 pro Gramm Kohlenhydrate, 9 pro Gramm Fett.<\/p>\n  <p>Zeigt ein Eintrag 100&nbsp;kcal, aber seine eigenen Makros ergeben 250, ist eine der beiden Zeilen falsch. Und deine App zeigt dir eine der beiden, ohne zu blinzeln.<\/p>\n  <div class=\"metho\">\n    <h3>Komplette Methodik<\/h3>\n    <ul>\n      <li>Offizieller OpenFoodFacts-Dump vom 23. August 2026 (ODbL-Lizenz), Frankreich-Scope<\/li>\n      <li>Neuberechnung: <code>4&times;Protein + 4&times;Kohlenhydrate + 9&times;Fett<\/code>, plus 7&nbsp;kcal\/g Alkohol, 2&nbsp;kcal\/g Ballaststoffe und 2,4&nbsp;kcal\/g Polyole (Erythrit: 0), gem\u00e4\u00df INCO-Verordnung 1169\/2011<\/li>\n      <li>Ein Eintrag z\u00e4hlt nur als inkonsistent, wenn der Abstand sowohl 10&nbsp;% als auch 30&nbsp;kcal\/100g \u00fcberschreitet: Das Rauschen von W\u00e4ssern und Tees bei 2&nbsp;kcal ist ausgeschlossen<\/li>\n      <li>16&#8239;364 aberrante Eintr\u00e4ge verworfen (Kalorien au\u00dferhalb 1-950&nbsp;kcal\/100g, unm\u00f6gliche Makros)<\/li>\n      <li>Jedes auf dieser Seite zitierte Beispiel wurde am Publikationstag gegen den Live-Eintrag gegengepr\u00fcft<\/li>\n    <\/ul>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"s3\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">03<\/span><\/div>\n  <h2 id=\"s3\">Ergebnis 1: Fast jeder 3. Eintrag ist unvollst\u00e4ndig<\/h2>\n  <div class=\"bignum\">31 %<small>der franz\u00f6sischen Produkte fehlen vollst\u00e4ndige Daten<\/small><\/div>\n  <p>391&#8239;707 in Frankreich verkaufte Produkte haben keine Kalorien hinterlegt, oder es fehlen die drei Makron\u00e4hrstoffe. Konkret: Du scannst, und die App zeigt 0&nbsp;kcal, oder Kalorien ohne Protein oder Kohlenhydrate. Du hast das Gef\u00fchl zu tracken; du loggst Leere.<\/p>\n  <figure class=\"fig rev\">\n    <div class=\"cv-wrap\" style=\"position:relative;width:100%;height:400px;min-height:360px\"><canvas id=\"chartDonut\" aria-label=\"Verteilung der 1 265 726 franz\u00f6sischen Produkte nach Zuverl\u00e4ssigkeit des Eintrags\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Das Schicksal eines Scans in Frankreich, \u00fcber 1&#8239;265&#8239;726 Produkte.<\/figcaption>\n  <\/figure>\n  <p>Es ist der heimt\u00fcckischste Fehler, weil er nicht wie ein Fehler aussieht: Der Eintrag wird angezeigt, das Produkt hat einen Namen, ein Foto. Nur das Wesentliche fehlt.<\/p>\n<\/section>\n\n<section aria-labelledby=\"s4\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">04<\/span><\/div>\n  <h2 id=\"s4\">Ergebnis 2: 46&#8239;757 Produkte mit inkonsistenten Kalorien<\/h2>\n  <p>Von den 857&#8239;655 vollst\u00e4ndigen Eintr\u00e4gen zeigen 46&#8239;757 Kalorien, die mit ihren eigenen Makros unvereinbar sind, um mindestens 10&nbsp;% und 30&nbsp;kcal pro 100&nbsp;g. Jeder Punkt im Chart unten ist ein echtes Produkt: auf der x-Achse, was sein Eintrag deklariert, auf der y-Achse, was seine Makros ergeben. Ein ehrlicher Eintrag landet auf der Diagonale.<\/p>\n  <figure class=\"fig rev\">\n    <div class=\"cv-wrap\" style=\"position:relative;width:100%;height:460px;min-height:420px\"><canvas id=\"chartScatter\" aria-label=\"Deklarierte Kalorien gegen\u00fcber neu berechneten Kalorien, Stichprobe von 1 370 Produkten\" role=\"img\"><\/canvas><\/div>\n    <figcaption>1&#8239;370 echte Produkte: deklariert vs. aus den Makros neu berechnet.<\/figcaption>\n  <\/figure>\n  <p>Die Verteilung der Abst\u00e4nde zeigt: Die Datenbank ist massiv korrekt und l\u00e4uft dann in einen langen Schwanz aus. Falsche Eintr\u00e4ge sind nicht leicht falsch, sie sind sehr falsch.<\/p>\n  <figure class=\"fig rev\">\n    <div class=\"cv-wrap\" style=\"position:relative;width:100%;height:360px;min-height:320px\"><canvas id=\"chartHisto\" aria-label=\"Verteilung der relativen Abweichungen zwischen deklarierten und neu berechneten Kalorien\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Verteilung der Abst\u00e4nde, logarithmische Skala.<\/figcaption>\n  <\/figure>\n  <figure class=\"fig rev\">\n    <div class=\"cv-wrap\" style=\"position:relative;width:100%;height:360px;min-height:320px\"><canvas id=\"chartSeuils\" aria-label=\"Inkonsistente Produkte nach Abweichungsschwelle\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Inkonsistente Produkte nach Abstands-Schwelle, von 857&#8239;655 Eintr\u00e4gen.<\/figcaption>\n  <\/figure>\n  <p>18&#8239;251 Produkte, zwei materielle Inkonsistenzen von f\u00fcnf, liegen daneben <em>um den Faktor zwei oder mehr<\/em>. Auf diesem Niveau ist es keine Ungenauigkeit mehr, sondern ein Datenpunkt, der deine t\u00e4gliche Kalorienbilanz kippt.<\/p>\n  <p>Unvollst\u00e4ndige und inkonsistente Eintr\u00e4ge zusammengez\u00e4hlt: <strong>Mehr als jeder dritte Scan in Frankreich liefert fehlende, unvollst\u00e4ndige oder inkonsistente Daten<\/strong>.<\/p>\n<\/section>\n\n<section aria-labelledby=\"s5\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">05<\/span><\/div>\n  <h2 id=\"s5\">Produkte aus deinem K\u00fcchenschrank<\/h2>\n  <p>Jeder Eintrag unten wurde am Publikationstag online gegengepr\u00fcft. Links, was der OpenFoodFacts-Eintrag anzeigt, rechts, was seine eigenen Makros ergeben:<\/p>\n  <div class=\"prod-cards\">\n    <div class=\"prod-card\"><div class=\"who\"><b>Le Beurre Tendre<\/b><span>Eintrag Elle&amp;Vire &middot; 535 Scans<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">375<\/span><span class=\"unit\">Eintrag<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">744<\/span><span class=\"unit\">Makros<\/span><\/span><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>Yaos Joghurt nach griechischer Art<\/b><span>Eintrag Nestl\u00e9 &middot; 271 Scans<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">50<\/span><span class=\"unit\">Eintrag<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">98<\/span><span class=\"unit\">Makros<\/span><\/span><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>Sweet &amp; Salty Nut Schokolade<\/b><span>Eintrag Nature Valley &middot; 338 Scans<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">647<\/span><span class=\"unit\">Eintrag<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">140<\/span><span class=\"unit\">Makros<\/span><\/span><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>Ratatouille \u00e0 la Proven\u00e7ale<\/b><span>Eintrag Cassegrain &middot; 298 Scans<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">38<\/span><span class=\"unit\">Eintrag<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">72<\/span><span class=\"unit\">Makros<\/span><\/span><\/div><\/div>\n  <\/div>\n  <p>Der Lehrbuchfall: Die Auvergne-Linsen von Raynal &amp; Roquelaure existieren unter <strong>zwei verschiedenen Eintr\u00e4gen<\/strong>. Einer zeigt 48&nbsp;kcal\/100g, der andere 207. Der echte Wert liegt um 99. Je nachdem, welchen Barcode deine App erwischt, glaubst du, die H\u00e4lfte oder das Doppelte der Realit\u00e4t zu essen.<\/p>\n  <p>Wer 30&nbsp;g der Butter oben aufs Brot streicht, z\u00e4hlt 110&nbsp;kcal zu wenig. Jeden Tag. Im Glauben, alles richtig zu machen.<\/p>\n  <div class=\"cta-band rev\">\n    <div class=\"l\">Lean pr\u00fcft Eintr\u00e4ge auf Konsistenz und kann deinen Teller auch per Foto scannen, ohne von einem Barcode abzuh\u00e4ngen.<\/div>\n    <div class=\"stores\">\n      <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n      <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"s6\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">06<\/span><\/div>\n  <h2 id=\"s6\">Die am st\u00e4rksten betroffenen Kategorien<\/h2>\n  <p>Die Inkonsistenz ist nicht zuf\u00e4llig verteilt, und die Ironie des Rankings ist grausam: Die am st\u00e4rksten betroffene Kategorie der gesamten Datenbank ist die, die Sportler am meisten tracken. <strong>Mehr als jeder f\u00fcnfte Proteinriegel (20,8&nbsp;%) hat einen inkonsistenten Eintrag.<\/strong><\/p>\n  <figure class=\"fig rev\">\n    <div class=\"cv-wrap\" style=\"position:relative;width:100%;height:460px;min-height:420px\"><canvas id=\"chartBubble\" aria-label=\"Kategorien: Produktvolumen, Rate und Anzahl inkonsistenter Eintr\u00e4ge\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Jede Blase ist eine Kategorie; ihre Gr\u00f6\u00dfe: ihre inkonsistenten Eintr\u00e4ge.<\/figcaption>\n  <\/figure>\n  <p>Komplexe Rezepturen und angereicherte Produkte konzentrieren die Fehler: Proteinriegel, S\u00fc\u00dfwaren, Bonbons, K\u00e4se. Einfache Produkte schneiden besser ab.<\/p>\n  <figure class=\"fig rev\">\n    <div class=\"cv-wrap\" style=\"position:relative;width:100%;height:380px;min-height:340px\"><canvas id=\"chartCats\" aria-label=\"Inkonsistenzrate nach Kategorie\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Anteil inkonsistenter Eintr\u00e4ge pro Kategorie (min. 1&#8239;000 Produkte).<\/figcaption>\n  <\/figure>\n  <p>Ein ehrlicher Hinweis: Bonbons, S\u00fc\u00dfwaren und Riegel erkl\u00e4ren sich teilweise durch S\u00fc\u00dfstoffe. Deklariert ein Eintrag seine Polyole nicht, \u00fcbersch\u00e4tzt die Neuberechnung den Abstand. Wir haben alles korrigiert, was deklariert war; der Rest spiegelt auch diese Deklarationsl\u00fccke, die deine App auf genau dieselbe Weise t\u00e4uscht.<\/p>\n<\/section>\n\n<section aria-labelledby=\"s7\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">07<\/span><\/div>\n  <h2 id=\"s7\">Was das f\u00fcr dein Tracking \u00e4ndert<\/h2>\n  <p>Zuerst, was das nicht bedeutet: Du solltest nicht aufh\u00f6ren zu scannen, und OpenFoodFacts bleibt ein bemerkenswertes Projekt, das lebt und sich laufend korrigiert. Die Kurve unten zeigt es: Eintr\u00e4ge von 2020-2021 sind die unzuverl\u00e4ssigsten (bis zu 7,5&nbsp;% Inkonsistenz), und die Qualit\u00e4t hat sich seit 2023 deutlich erholt.<\/p>\n  <figure class=\"fig rev\">\n    <div class=\"cv-wrap\" style=\"position:relative;width:100%;height:340px;min-height:300px\"><canvas id=\"chartYears\" aria-label=\"Rate inkonsistenter Eintr\u00e4ge nach Erstellungsjahr des Eintrags\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Inkonsistenz-Rate nach Erstellungsjahr des Eintrags.<\/figcaption>\n  <\/figure>\n  <p>Vier der sechs schlimmsten Eintr\u00e4ge des Dumps waren online schon korrigiert, als wir sie pr\u00fcften. Aber deine App kann die falsche Version gesynct haben, und die Hunderttausenden Eintr\u00e4ge aus den schlechtesten Jahren sind noch in der Datenbank.<\/p>\n  <p>Zwei Reflexe zum Mitnehmen:<\/p>\n  <p><strong>1. Scannen ist ein Geschwindigkeits-Tool, kein Wahrheits-Tool.<\/strong> Nach einem Scan gen\u00fcgt ein Blick auf die Makros: Passen Protein, Kohlenhydrate und Fett nicht zu den angezeigten Kalorien, ist der Eintrag falsch. Das ist exakt der Test dieser Studie, und er dauert zwei Sekunden.<\/p>\n  <p><strong>2. Der unsichtbare Fehler liegt nicht auf deinem Teller, sondern in deinem Verbrauch.<\/strong> Ein falscher Eintrag mit 110&nbsp;kcal Abweichung ist in zehn Sekunden korrigiert, sobald er auff\u00e4llt. Ein Tagesverbrauch, der um 300&nbsp;kcal falsch liegt, ist nie sichtbar: Er steckt im &bdquo;Aktivit\u00e4tsmultiplikator&ldquo;, den deine App dich bei der Anmeldung w\u00e4hlen lie\u00df. Genau dort konzentriert Lean seine Pr\u00e4zision: <a href=\"https:\/\/lean-app.com\/de\/depense-energetique-totale-v2\/\">TDEE = BMR + NEAT + EAT + TEF<\/a>, mit einem BMR, berechnet auf deinem <a href=\"https:\/\/lean-app.com\/de\/calcul-metabolisme-de-base\/\">echten K\u00f6rperfett<\/a> und einem <a href=\"https:\/\/lean-app.com\/de\/calculateur-neat\/\">NEAT auf Basis deiner echten Schritte<\/a>, nicht auf einem angekreuzten K\u00e4stchen.<\/p>\n  <div class=\"cta-band rev\">\n    <div class=\"l\">Werde Lean, bleib Lean: die einzige App, die deinen Verbrauch aus Messungen berechnet, nicht aus K\u00e4stchen.<\/div>\n    <div class=\"stores\">\n      <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n      <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"s9\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">08<\/span><\/div>\n  <h2 id=\"s9\">Wissenschaftliche Repositorien und Zitation<\/h2>\n  <p>Diese Studie ist in unabh\u00e4ngigen offenen Archiven hinterlegt, mit einem persistenten Identifikator (DOI) und den vollst\u00e4ndigen PDFs auf Franz\u00f6sisch und Englisch. Jede Hinterlegung ist ohne Umweg \u00fcber diese Website \u00fcberpr\u00fcfbar:<\/p>\n  <div class=\"prod-cards\">\n    <div class=\"prod-card\"><div class=\"who\"><b>Zenodo (CERN)<\/b><span>DOI 10.5281\/zenodo.22284416 \u00b7 Referenzhinterlegung, PDF FR + EN<\/span><\/div><div class=\"vals\"><a href=\"https:\/\/doi.org\/10.5281\/zenodo.22284416\" target=\"_blank\" rel=\"noopener\">Hinterlegung \u00f6ffnen \u2192<\/a><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>Figshare<\/b><span>DOI 10.6084\/m9.figshare.33432244 \u00b7 Preprint, PDF FR + EN<\/span><\/div><div class=\"vals\"><a href=\"https:\/\/doi.org\/10.6084\/m9.figshare.33432244\" target=\"_blank\" rel=\"noopener\">Hinterlegung \u00f6ffnen \u2192<\/a><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>OSF (Center for Open Science)<\/b><span>\u00f6ffentliches Projekt tczvj \u00b7 Methodik und Dateien<\/span><\/div><div class=\"vals\"><a href=\"https:\/\/osf.io\/tczvj\/\" target=\"_blank\" rel=\"noopener\">Projekt \u00f6ffnen \u2192<\/a><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>data.gouv.fr<\/b><span>deklarierte Nachnutzung des Open-Food-Facts-Datensatzes<\/span><\/div><div class=\"vals\"><a href=\"https:\/\/www.data.gouv.fr\/fr\/reuses\/etude-coherence-interne-des-donnees-caloriques-dopen-food-facts\/\" target=\"_blank\" rel=\"noopener\">Nachnutzung ansehen \u2192<\/a><\/div><\/div>\n  <\/div>\n  <p><strong>So zitieren Sie diese Studie:<\/strong> Das Lean-Team (2026). 857 655 analysierte Produkte: interne Koh\u00e4renz der Kaloriendaten von Open Food Facts. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.22284416<\/p>\n<\/section>\n\n<section aria-labelledby=\"s8\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">FAQ<\/span><\/div>\n  <h2 id=\"s8\">H\u00e4ufige Fragen<\/h2>\n  <div class=\"faq\">\n    <details><summary>Warum OpenFoodFacts und nicht USDA?<\/summary><div class=\"ans\">F\u00fcr Barcode-Produkte in Frankreich ist die Referenzquelle der Tracker OpenFoodFacts. USDA deckt Grundnahrungsmittel und den US-Markt ab. Diese Studie handelt davon, was ein Scan in Frankreich tats\u00e4chlich zur\u00fcckliefert.<\/div><\/details>\n    <details><summary>Solltest du aufh\u00f6ren, dein Essen zu scannen?<\/summary><div class=\"ans\">Nein. Scannen bleibt der schnellste Weg zu loggen. Die gute Praxis: Pr\u00fcfe, ob die angezeigten Makros zu den Kalorien passen. Zwei Sekunden, die die schlimmsten Eintr\u00e4ge vermeiden.<\/div><\/details>\n    <details><summary>Korrigieren die Apps diese Fehler?<\/summary><div class=\"ans\">Die Datenbank lebt und korrigiert sich laufend, das ist die St\u00e4rke der Kollaboration. Aber eine App, die einen falschen Eintrag gesynct hat, kann ihn lange nach der Online-Korrektur im Cache behalten.<\/div><\/details>\n    <details><summary>D\u00fcrfen die Zahlen dieser Studie weiterverwendet werden?<\/summary><div class=\"ans\">Ja, frei, mit Nennung &bdquo;Lean-Studie, August 2026&ldquo; und Link auf diese Seite. Die Quelldaten geh\u00f6ren OpenFoodFacts (ODbL-Lizenz), und unsere Methodik ist oben vollst\u00e4ndig beschrieben.<\/div><\/details>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"src\" class=\"sources rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">Quellen<\/span><\/div>\n  <ol>\n    <li>OpenFoodFacts, offizieller Dump vom 23. August 2026, <a href=\"https:\/\/world.openfoodfacts.org\/data\" target=\"_blank\" rel=\"noopener\">world.openfoodfacts.org\/data<\/a>, ODbL-Lizenz.<\/li>\n    <li>Verordnung (EU) Nr. 1169\/2011 (INCO), Anhang XIV: Energie-Umrechnungsfaktoren.<\/li>\n    <li>Atwater W.O. &amp; Bryant A.P. (1900). The availability and fuel value of food materials.<\/li>\n    <li>Merrill A.L. &amp; Watt B.K. (1973). Energy value of foods, USDA Agriculture Handbook No. 74.<\/li>\n  <\/ol>\n<\/section>\n\n<\/main>\n\n<footer>\n  <div class=\"wrap\">\n    <div class=\"row\">\n      <div>\n        <div class=\"kicker\">Lean &middot; lean-app.com<\/div>\n        <p>Studie ver\u00f6ffentlicht am 24. August 2026. Regelm\u00e4\u00dfig aktualisiert, sobald neue OpenFoodFacts-Dumps analysiert werden. Lean ist auf iOS und Android verf\u00fcgbar.<\/p>\n      <\/div>\n      <div class=\"stores\">\n        <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n        <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=etude-off\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/><\/a>\n      <\/div>\n    <\/div>\n  <\/div>\n<\/footer>\n<script data-wpmeteor-nooptimize=\"true\">\n(function(){\n  var bar = document.getElementById('progBar');\n  function up(){\n    var h = document.documentElement;\n    var sc = (h.scrollTop)\/Math.max(1,(h.scrollHeight - h.clientHeight));\n    bar.style.transform = 'scaleX(' + Math.max(0,Math.min(1,sc)) + ')';\n  }\n  document.addEventListener('scroll', up, {passive:true});\n  up();\n})();\n\n(function(){\n  if (!('IntersectionObserver' in window)) {\n    document.querySelectorAll('.rev').forEach(function(n){n.classList.add('on')});\n    return;\n  }\n  var obs = new IntersectionObserver(function(entries){\n    entries.forEach(function(e){\n      if (e.isIntersecting) { e.target.classList.add('on'); obs.unobserve(e.target); }\n    });\n  }, {threshold:0.12});\n  document.querySelectorAll('.rev').forEach(function(n){ obs.observe(n); });\n})();\n<\/script>\n\n<script data-wpmeteor-nooptimize=\"true\">\n\/\/ === Charts etude OFF ===\n(function chartInit(){\n  if (typeof Chart === 'undefined') { return setTimeout(chartInit, 60); }\n  var LEAN_LANG = document.documentElement.lang || 'fr';\n  function T(fr, en){ return LEAN_LANG.indexOf('en') === 0 ? en : fr; }\n  var PINK = '#FF2D6E', INK = '#0E0E10', MUTED = '#6E6E73', RULE = '#E8E6DF';\n  var PINKS = ['rgba(255,45,110,1)','rgba(255,45,110,0.82)','rgba(255,45,110,0.66)','rgba(255,45,110,0.52)','rgba(255,45,110,0.4)','rgba(255,45,110,0.3)','rgba(255,45,110,0.24)','rgba(255,45,110,0.18)'];\n  Chart.defaults.font.family = '-apple-system, \"SF Pro Text\", system-ui, sans-serif';\n  var DPR = Math.max(2, window.devicePixelRatio || 2);\n  function nf(v){ return v.toLocaleString('fr-FR'); }\n  function pinkGrad(ctx, alphaTop){\n    var g = ctx.createLinearGradient(0, 0, 0, ctx.canvas.clientHeight || 360);\n    g.addColorStop(0, 'rgba(255,45,110,' + alphaTop + ')');\n    g.addColorStop(1, 'rgba(255,45,110,0.02)');\n    return g;\n  }\n\n  \/\/ 1. 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Ce que ton scanner de calories ne te dit pas. Chaque scan de code-barres en France interroge la m\u00eame base. Nous l&rsquo;avons analys\u00e9e en entier. Plus d&rsquo;un scan sur trois renvoie une donn\u00e9e absente, incompl\u00e8te ou incoh\u00e9rente. [&hellip;]<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-lvm-blank","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1947","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Wir haben 857 655 in Frankreich verkaufte Produkte analysiert: was dir dein Kalorienscanner nicht sagt - Lean<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lean-app.com\/de\/etude-base-donnees-calories\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Wir haben 857 655 in Frankreich verkaufte Produkte analysiert: was dir dein Kalorienscanner nicht sagt - Lean\" \/>\n<meta property=\"og:description\" content=\"Lean Calculateur TDEE Accueil &nbsp;\/&nbsp; \u00c9tude base de donn\u00e9es calories \u00c9tude originale &middot; Donn\u00e9es publiques 857&nbsp;655 produits analys\u00e9s. Ce que ton scanner de calories ne te dit pas. Chaque scan de code-barres en France interroge la m\u00eame base. Nous l&rsquo;avons analys\u00e9e en entier. Plus d&rsquo;un scan sur trois renvoie une donn\u00e9e absente, incompl\u00e8te ou incoh\u00e9rente. 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Ce que ton scanner de calories ne te dit pas. Chaque scan de code-barres en France interroge la m\u00eame base. Nous l&rsquo;avons analys\u00e9e en entier. Plus d&rsquo;un scan sur trois renvoie une donn\u00e9e absente, incompl\u00e8te ou incoh\u00e9rente. 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