{"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\/pt\/etude-base-donnees-calories\/","title":{"rendered":"Analisamos 857 655 produtos vendidos na Fran\u00e7a: o que seu scanner de calorias n\u00e3o te diz"},"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 main.wrap{display:block!important;max-width:760px!important;margin-left:auto!important;margin-right:auto!important;padding-left:28px!important;padding-right:28px!important}\n@media (max-width:820px){\n  body.postid-1947 #lvm-shell .wrap,\n  body.postid-1947 #lvm-shell main.wrap{padding-left:18px!important;padding-right:18px!important}\n}\nhtml, body{overflow-x:hidden!important}\nbody.postid-1947 #lvm-shell{overflow-x:hidden;max-width:100vw}\nbody.postid-1947 #lvm-shell *{max-width:100%}\nbody.postid-1947 #lvm-shell .nav-row{max-width:100vw;box-sizing:border-box}\nbody.postid-1947 #lvm-shell.force-show .rev{opacity:1!important;transform:none!important}\n@media (max-width:760px){\n  body.postid-1947 #lvm-shell .cta-band{flex-direction:column!important;align-items:center!important;text-align:center!important;padding:26px 22px!important;gap:20px!important}\n  body.postid-1947 #lvm-shell .cta-band .l{min-width:0!important;width:100%!important;font-size:16px!important;line-height:1.5!important;text-align:center!important}\n  body.postid-1947 #lvm-shell .cta-band .stores{width:100%!important;justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-1947 #lvm-shell .cta-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-1947 #lvm-shell .cta-band .stores img{height:56px!important;width:100%!important;max-width:170px!important;object-fit:contain!important;object-position:center!important;border-radius:10px!important}\n}\n<\/style>\n<div id=\"lvm-shell\">\n<div class=\"progress\" aria-hidden=\"true\"><i id=\"progBar\"><\/i><\/div>\n\n<header class=\"nav\">\n  <div class=\"nav-row\">\n    <a class=\"nav-brand\" href=\"https:\/\/lean-app.com\/pt\/\" aria-label=\"In\u00edcio Lean\">\n      <img 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\/pt\/tdee-calculator\/\">Calculadora TDEE<\/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=\"Baixar na App Store\">\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=\"Dispon\u00edvel no 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\/pt\/\">In\u00edcio<\/a> &nbsp;\/&nbsp; Estudo banco de dados de calorias<\/div>\n  <div class=\"eyebrow\">Estudo original &middot; Dados p\u00fablicos<\/div>\n  <h1 id=\"title\">857&#8239;655 produtos analisados.\n    <span class=\"alt\">O que seu scanner de calorias n\u00e3o te conta.<\/span>\n  <\/h1>\n  <p class=\"dek\">Cada escaneamento de c\u00f3digo de barras na Fran\u00e7a consulta o mesmo banco de dados. Analisamos ele inteiro. Mais de um escaneamento em cada tr\u00eas devolve dados ausentes, incompletos ou incoerentes.<\/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>Por <strong>A equipe Lean<\/strong> &middot; publicado em 24 de agosto de 2026 &middot; 7 min de leitura<\/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=\"Baixar o Lean na App Store\">\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=\"Baixar o Lean no Google Play\">\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\">Por que este estudo<\/h2>\n  <p>Quando voc\u00ea escaneia um c\u00f3digo de barras num tracker de calorias na Fran\u00e7a, seja MyFitnessPal, Yazio, Lifesum ou Lean, os dados v\u00eam mais ou menos da mesma fonte: <a href=\"https:\/\/world.openfoodfacts.org\/\" target=\"_blank\" rel=\"noopener\">OpenFoodFacts<\/a>, um not\u00e1vel banco de dados colaborativo franc\u00eas sob licen\u00e7a aberta ODbL. Milh\u00f5es de fichas de produtos, preenchidas por colaboradores volunt\u00e1rios.<\/p>\n  <p>Um banco colaborativo \u00e9 ao mesmo tempo sua for\u00e7a e seu limite: qualquer um pode criar ou editar uma ficha, e ningu\u00e9m verifica sistematicamente. Ningu\u00e9m tinha nunca quantificado a confiabilidade real do que seu escaneamento devolve. Ent\u00e3o baixamos o dump p\u00fablico completo de 23 de agosto de 2026 e testamos cada produto vendido na Fran\u00e7a.<\/p>\n  <div class=\"stat-grid\">\n    <div class=\"stat-card\"><div class=\"n\">4 535 553<\/div><div class=\"t\">produtos no banco mundial em 23 de agosto de 2026<\/div><\/div>\n    <div class=\"stat-card\"><div class=\"n\">1 265 726<\/div><div class=\"t\">produtos listados como vendidos na Fran\u00e7a<\/div><\/div>\n    <div class=\"stat-card\"><div class=\"n pink\">857 655<\/div><div class=\"t\">produtos analis\u00e1veis: calorias e os 3 macros preenchidos<\/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\">O m\u00e9todo: cada ficha testada contra si mesma<\/h2>\n  <p>N\u00e3o \u00e9 preciso laborat\u00f3rio para detectar uma ficha errada: basta confront\u00e1-la consigo mesma. As calorias de um alimento podem ser deduzidas dos seus macronutrientes com os fatores de Atwater, os mesmos que o regulamento europeu INCO imp\u00f5e aos fabricantes: 4&nbsp;kcal por grama de prote\u00edna, 4 por grama de carboidrato, 9 por grama de gordura.<\/p>\n  <p>Se uma ficha mostra 100&nbsp;kcal mas seus pr\u00f3prios macros somam 250, uma das duas linhas est\u00e1 errada. E seu app mostra uma das duas sem piscar.<\/p>\n  <div class=\"metho\">\n    <h3>Metodologia completa<\/h3>\n    <ul>\n      <li>Dump oficial do OpenFoodFacts de 23 de agosto de 2026 (licen\u00e7a ODbL), escopo Fran\u00e7a<\/li>\n      <li>Rec\u00e1lculo: <code>4&times;prote\u00ednas + 4&times;carboidratos + 9&times;gorduras<\/code>, mais 7&nbsp;kcal\/g de \u00e1lcool, 2&nbsp;kcal\/g de fibra e 2,4&nbsp;kcal\/g de poli\u00f3is (eritritol: 0), conforme o regulamento INCO 1169\/2011<\/li>\n      <li>Uma ficha s\u00f3 conta como incoerente se a diferen\u00e7a ultrapassa ao mesmo tempo 10&nbsp;% e 30&nbsp;kcal\/100g: o ru\u00eddo de \u00e1guas e ch\u00e1s a 2&nbsp;kcal fica exclu\u00eddo<\/li>\n      <li>16&#8239;364 fichas aberrantes descartadas (calorias fora de 1-950&nbsp;kcal\/100g, macros imposs\u00edveis)<\/li>\n      <li>Cada exemplo citado nesta p\u00e1gina foi verificado de novo contra a ficha online no dia da publica\u00e7\u00e3o<\/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\">Resultado 1: quase 1 ficha em cada 3 est\u00e1 incompleta<\/h2>\n  <div class=\"bignum\">31 %<small>dos produtos franceses n\u00e3o t\u00eam dados completos<\/small><\/div>\n  <p>391&#8239;707 produtos vendidos na Fran\u00e7a n\u00e3o t\u00eam calorias preenchidas, ou faltam os tr\u00eas macronutrientes. Em concreto: voc\u00ea escaneia, e o app mostra 0&nbsp;kcal, ou calorias sem prote\u00ednas nem carboidratos. Voc\u00ea acha que est\u00e1 trackeando; est\u00e1 registrando vazio.<\/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=\"Distribui\u00e7\u00e3o dos 1 265 726 produtos franceses por confiabilidade da ficha\" role=\"img\"><\/canvas><\/div>\n    <figcaption>O destino de um escaneamento na Fran\u00e7a, sobre 1&#8239;265&#8239;726 produtos.<\/figcaption>\n  <\/figure>\n  <p>\u00c9 o erro mais trai\u00e7oeiro, porque n\u00e3o parece um erro: a ficha aparece, o produto tem nome, foto. S\u00f3 falta o essencial.<\/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\">Resultado 2: 46&#8239;757 produtos com calorias incoerentes<\/h2>\n  <p>Das 857&#8239;655 fichas completas, 46&#8239;757 mostram calorias incompat\u00edveis com seus pr\u00f3prios macros, em pelo menos 10&nbsp;% e 30&nbsp;kcal por 100&nbsp;g. Cada ponto do gr\u00e1fico abaixo \u00e9 um produto real: no eixo x o que sua ficha declara, no eixo y o que seus macros d\u00e3o. Uma ficha honesta cai na diagonal.<\/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=\"Calorias declaradas contra calorias recalculadas, amostra de 1 370 produtos\" role=\"img\"><\/canvas><\/div>\n    <figcaption>1&#8239;370 produtos reais: declarado vs recalculado a partir dos macros.<\/figcaption>\n  <\/figure>\n  <p>A distribui\u00e7\u00e3o das diferen\u00e7as mostra que o banco \u00e9 massivamente correto, e depois se alonga numa cauda longa: as fichas erradas n\u00e3o est\u00e3o um pouco erradas, est\u00e3o muito erradas.<\/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=\"Distribui\u00e7\u00e3o das diferen\u00e7as relativas entre calorias declaradas e recalculadas\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Distribui\u00e7\u00e3o das diferen\u00e7as, escala logar\u00edtmica.<\/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=\"Produtos incoerentes por limiar de diferen\u00e7a\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Produtos incoerentes por limiar de diferen\u00e7a, de 857&#8239;655 fichas.<\/figcaption>\n  <\/figure>\n  <p>18&#8239;251 produtos, duas incoer\u00eancias materiais em cada cinco, desviam <em>do simples ao dobro ou mais<\/em>. Nesse n\u00edvel, j\u00e1 n\u00e3o \u00e9 uma imprecis\u00e3o, \u00e9 um dado que vira seu balan\u00e7o cal\u00f3rico di\u00e1rio.<\/p>\n  <p>Somando fichas incompletas e incoerentes: <strong>mais de um escaneamento em cada tr\u00eas na Fran\u00e7a devolve dados ausentes, incompletos ou incoerentes<\/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\">Produtos que est\u00e3o no seu arm\u00e1rio<\/h2>\n  <p>Cada ficha abaixo foi verificada online no dia da publica\u00e7\u00e3o. \u00c0 esquerda o que a ficha do OpenFoodFacts mostra, \u00e0 direita o que seus pr\u00f3prios macros d\u00e3o:<\/p>\n  <div class=\"prod-cards\">\n    <div class=\"prod-card\"><div class=\"who\"><b>Le Beurre Tendre<\/b><span>Ficha Elle&amp;Vire &middot; 535 escaneamentos<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">375<\/span><span class=\"unit\">ficha<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">744<\/span><span class=\"unit\">macros<\/span><\/span><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>Iogurte estilo grego Yaos<\/b><span>Ficha Nestl\u00e9 &middot; 271 escaneamentos<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">50<\/span><span class=\"unit\">ficha<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">98<\/span><span class=\"unit\">macros<\/span><\/span><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>Sweet &amp; Salty Nut chocolate<\/b><span>Ficha Nature Valley &middot; 338 escaneamentos<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">647<\/span><span class=\"unit\">ficha<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">140<\/span><span class=\"unit\">macros<\/span><\/span><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>Ratatouille \u00e0 la Proven\u00e7ale<\/b><span>Ficha Cassegrain &middot; 298 escaneamentos<\/span><\/div><div class=\"vals\"><span><span class=\"fiche\">38<\/span><span class=\"unit\">ficha<\/span><\/span><span class=\"fleche\">&rarr;<\/span><span><span class=\"reel\">72<\/span><span class=\"unit\">macros<\/span><\/span><\/div><\/div>\n  <\/div>\n  <p>O caso de manual: as lentilhas \u00e0 moda de Auvergne da Raynal &amp; Roquelaure existem sob <strong>duas fichas diferentes<\/strong>. Uma mostra 48&nbsp;kcal\/100g, a outra 207. O valor real fica em torno de 99. Dependendo de qual c\u00f3digo de barras seu app pega, voc\u00ea acha que come metade ou o dobro da realidade.<\/p>\n  <p>Quem passa 30&nbsp;g da manteiga acima conta 110&nbsp;kcal a menos. Todo dia. Achando que est\u00e1 fazendo certo.<\/p>\n  <div class=\"cta-band rev\">\n    <div class=\"l\">O Lean verifica a coer\u00eancia das fichas e tamb\u00e9m consegue escanear seu prato a partir de uma foto, sem depender de um c\u00f3digo de barras.<\/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\">As categorias mais afetadas<\/h2>\n  <p>A incoer\u00eancia n\u00e3o se distribui ao acaso, e a ironia do ranking \u00e9 cruel: a categoria mais afetada de todo o banco \u00e9 a que os esportistas mais trackeiam. <strong>Mais de uma barra de prote\u00edna em cada cinco (20,8&nbsp;%) tem uma ficha incoerente.<\/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=\"Categorias: volume de produtos, taxa e n\u00famero de fichas incoerentes\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Cada bolha \u00e9 uma categoria; seu tamanho, suas fichas incoerentes.<\/figcaption>\n  <\/figure>\n  <p>As receitas complexas e os produtos enriquecidos concentram os erros: barras de prote\u00edna, doces, balas, queijos. Os produtos simples se saem melhor.<\/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=\"Taxa de incoer\u00eancia por categoria\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Parte de fichas incoerentes por categoria (m\u00edn. 1&#8239;000 produtos).<\/figcaption>\n  <\/figure>\n  <p>Uma nota de honestidade: balas, doces e barras se explicam em parte pelos ado\u00e7antes. Quando uma ficha n\u00e3o declara seus poli\u00f3is, o rec\u00e1lculo superestima a diferen\u00e7a. Corrigimos tudo o que foi declarado; o resto reflete tamb\u00e9m essa lacuna de declara\u00e7\u00e3o, que engana seu app exatamente da mesma maneira.<\/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\">O que isso muda para o seu tracking<\/h2>\n  <p>Primeiro, o que isso n\u00e3o significa: voc\u00ea n\u00e3o deveria parar de escanear, e o OpenFoodFacts continua sendo um projeto not\u00e1vel que vive e se corrige continuamente. A curva abaixo mostra isso: as fichas criadas em 2020-2021 s\u00e3o as menos confi\u00e1veis (at\u00e9 7,5&nbsp;% de incoer\u00eancia), e a qualidade se recuperou claramente desde 2023.<\/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=\"Taxa de fichas incoerentes segundo o ano de cria\u00e7\u00e3o da ficha\" role=\"img\"><\/canvas><\/div>\n    <figcaption>Taxa de incoer\u00eancia conforme o ano de cria\u00e7\u00e3o da ficha.<\/figcaption>\n  <\/figure>\n  <p>Quatro das seis piores fichas do dump j\u00e1 tinham sido corrigidas online quando verificamos. Mas seu app pode ter sincronizado a vers\u00e3o errada, e as centenas de milhares de fichas criadas nos piores anos continuam no banco.<\/p>\n  <p>Dois reflexos para guardar:<\/p>\n  <p><strong>1. Escanear \u00e9 uma ferramenta de velocidade, n\u00e3o de verdade.<\/strong> Depois de um escaneamento, basta um olhar nos macros: se prote\u00ednas, carboidratos e gorduras n\u00e3o batem com as calorias mostradas, a ficha est\u00e1 errada. \u00c9 exatamente o teste deste estudo, e leva dois segundos.<\/p>\n  <p><strong>2. O erro invis\u00edvel n\u00e3o est\u00e1 no seu prato, est\u00e1 no seu gasto.<\/strong> Uma ficha errada com 110&nbsp;kcal de desvio se corrige em dez segundos assim que detectada. Um gasto di\u00e1rio errado em 300&nbsp;kcal nunca \u00e9 vis\u00edvel: est\u00e1 enterrado no &laquo;multiplicador de atividade&raquo; que seu app fez voc\u00ea escolher no cadastro. \u00c9 a\u00ed que o Lean concentra sua precis\u00e3o: <a href=\"https:\/\/lean-app.com\/pt\/depense-energetique-totale-v2\/\">TDEE = BMR + NEAT + EAT + TEF<\/a>, com um BMR calculado sobre sua <a href=\"https:\/\/lean-app.com\/pt\/calcul-metabolisme-de-base\/\">gordura corporal real<\/a> e um <a href=\"https:\/\/lean-app.com\/pt\/calculateur-neat\/\">NEAT baseado nos seus passos reais<\/a>, n\u00e3o numa caixinha marcada.<\/p>\n  <div class=\"cta-band rev\">\n    <div class=\"l\">Fique Lean, continue Lean: o \u00fanico app que calcula seu gasto a partir de medidas, n\u00e3o de caixinhas.<\/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\">Reposit\u00f3rios cient\u00edficos e cita\u00e7\u00e3o<\/h2>\n  <p>Este estudo est\u00e1 depositado em arquivos abertos independentes, com um identificador persistente (DOI) e os PDFs completos em franc\u00eas e ingl\u00eas. Cada dep\u00f3sito pode ser verificado sem passar por este site:<\/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 dep\u00f3sito de refer\u00eancia, PDF FR + EN<\/span><\/div><div class=\"vals\"><a href=\"https:\/\/doi.org\/10.5281\/zenodo.22284416\" target=\"_blank\" rel=\"noopener\">Abrir o dep\u00f3sito \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\">Abrir o dep\u00f3sito \u2192<\/a><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>OSF (Center for Open Science)<\/b><span>projeto p\u00fablico tczvj \u00b7 metodologia e ficheiros<\/span><\/div><div class=\"vals\"><a href=\"https:\/\/osf.io\/tczvj\/\" target=\"_blank\" rel=\"noopener\">Abrir o projeto \u2192<\/a><\/div><\/div>\n    <div class=\"prod-card\"><div class=\"who\"><b>data.gouv.fr<\/b><span>reutiliza\u00e7\u00e3o declarada do conjunto de dados Open Food Facts<\/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\">Ver a reutiliza\u00e7\u00e3o \u2192<\/a><\/div><\/div>\n  <\/div>\n  <p><strong>Para citar este estudo:<\/strong> A equipa Lean (2026). 857 655 produtos analisados: coer\u00eancia interna dos dados cal\u00f3ricos do 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\">Perguntas frequentes<\/h2>\n  <div class=\"faq\">\n    <details><summary>Por que OpenFoodFacts e n\u00e3o USDA?<\/summary><div class=\"ans\">Para os produtos com c\u00f3digo de barras vendidos na Fran\u00e7a, a fonte de refer\u00eancia dos trackers \u00e9 o OpenFoodFacts. O USDA cobre alimentos b\u00e1sicos e o mercado americano. Este estudo trata do que um escaneamento na Fran\u00e7a realmente devolve.<\/div><\/details>\n    <details><summary>Voc\u00ea deveria parar de escanear sua comida?<\/summary><div class=\"ans\">N\u00e3o. Escanear continua sendo a forma mais r\u00e1pida de registrar. A boa pr\u00e1tica: verificar se os macros mostrados s\u00e3o coerentes com as calorias. Dois segundos que evitam as piores fichas.<\/div><\/details>\n    <details><summary>Os aplicativos corrigem esses erros?<\/summary><div class=\"ans\">O banco vive e se corrige continuamente, \u00e9 a for\u00e7a da colabora\u00e7\u00e3o. Mas um app que sincronizou uma ficha errada pode mant\u00ea-la em cache muito depois da corre\u00e7\u00e3o online.<\/div><\/details>\n    <details><summary>Os n\u00fameros deste estudo podem ser reutilizados?<\/summary><div class=\"ans\">Sim, livremente, citando &laquo;Estudo Lean, agosto de 2026&raquo; com um link para esta p\u00e1gina. Os dados fonte pertencem ao OpenFoodFacts (licen\u00e7a ODbL) e nossa metodologia est\u00e1 descrita integralmente acima.<\/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\">Fontes<\/span><\/div>\n  <ol>\n    <li>OpenFoodFacts, dump oficial de 23 de agosto de 2026, <a href=\"https:\/\/world.openfoodfacts.org\/data\" target=\"_blank\" rel=\"noopener\">world.openfoodfacts.org\/data<\/a>, licen\u00e7a ODbL.<\/li>\n    <li>Regulamento (UE) n.\u00ba 1169\/2011 (INCO), anexo XIV: fatores de convers\u00e3o energ\u00e9tica.<\/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>Estudo publicado em 24 de agosto de 2026. Atualizado com regularidade \u00e0 medida que novos dumps do OpenFoodFacts s\u00e3o analisados. O Lean est\u00e1 dispon\u00edvel em iOS e Android.<\/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>Analisamos 857 655 produtos vendidos na Fran\u00e7a: o que seu scanner de calorias n\u00e3o te diz - 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\/pt\/etude-base-donnees-calories\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analisamos 857 655 produtos vendidos na Fran\u00e7a: o que seu scanner de calorias n\u00e3o te diz - 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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